Overview

Brought to you by YData

Dataset statistics

Number of variables37
Number of observations1670214
Missing cells11109336
Missing cells (%)18.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 GiB
Average record size in memory1.2 KiB

Variable types

Numeric19
Categorical17
Boolean1

Alerts

AMT_ANNUITY is highly overall correlated with AMT_APPLICATION and 2 other fieldsHigh correlation
AMT_APPLICATION is highly overall correlated with AMT_ANNUITY and 3 other fieldsHigh correlation
AMT_CREDIT is highly overall correlated with AMT_ANNUITY and 4 other fieldsHigh correlation
AMT_DOWN_PAYMENT is highly overall correlated with RATE_DOWN_PAYMENTHigh correlation
AMT_GOODS_PRICE is highly overall correlated with AMT_ANNUITY and 3 other fieldsHigh correlation
CHANNEL_TYPE is highly overall correlated with NAME_CONTRACT_TYPE and 2 other fieldsHigh correlation
CNT_PAYMENT is highly overall correlated with AMT_APPLICATION and 3 other fieldsHigh correlation
CODE_REJECT_REASON is highly overall correlated with NAME_CONTRACT_STATUSHigh correlation
DAYS_DECISION is highly overall correlated with DAYS_FIRST_DUE and 4 other fieldsHigh correlation
DAYS_FIRST_DRAWING is highly overall correlated with FLAG_LAST_APPL_PER_CONTRACT and 6 other fieldsHigh correlation
DAYS_FIRST_DUE is highly overall correlated with DAYS_DECISION and 10 other fieldsHigh correlation
DAYS_LAST_DUE is highly overall correlated with DAYS_DECISION and 6 other fieldsHigh correlation
DAYS_LAST_DUE_1ST_VERSION is highly overall correlated with AMT_CREDIT and 12 other fieldsHigh correlation
DAYS_TERMINATION is highly overall correlated with DAYS_DECISION and 10 other fieldsHigh correlation
FLAG_LAST_APPL_PER_CONTRACT is highly overall correlated with DAYS_FIRST_DRAWING and 8 other fieldsHigh correlation
NAME_CASH_LOAN_PURPOSE is highly overall correlated with NAME_CONTRACT_TYPE and 4 other fieldsHigh correlation
NAME_CONTRACT_STATUS is highly overall correlated with CODE_REJECT_REASON and 10 other fieldsHigh correlation
NAME_CONTRACT_TYPE is highly overall correlated with CHANNEL_TYPE and 13 other fieldsHigh correlation
NAME_GOODS_CATEGORY is highly overall correlated with NAME_CONTRACT_TYPE and 1 other fieldsHigh correlation
NAME_PAYMENT_TYPE is highly overall correlated with DAYS_FIRST_DRAWING and 1 other fieldsHigh correlation
NAME_PORTFOLIO is highly overall correlated with CHANNEL_TYPE and 12 other fieldsHigh correlation
NAME_PRODUCT_TYPE is highly overall correlated with NAME_CASH_LOAN_PURPOSE and 4 other fieldsHigh correlation
NAME_SELLER_INDUSTRY is highly overall correlated with NAME_CONTRACT_TYPE and 2 other fieldsHigh correlation
NAME_YIELD_GROUP is highly overall correlated with DAYS_FIRST_DRAWING and 7 other fieldsHigh correlation
NFLAG_INSURED_ON_APPROVAL is highly overall correlated with FLAG_LAST_APPL_PER_CONTRACT and 5 other fieldsHigh correlation
NFLAG_LAST_APPL_IN_DAY is highly overall correlated with FLAG_LAST_APPL_PER_CONTRACTHigh correlation
PRODUCT_COMBINATION is highly overall correlated with DAYS_FIRST_DRAWING and 11 other fieldsHigh correlation
RATE_DOWN_PAYMENT is highly overall correlated with AMT_DOWN_PAYMENTHigh correlation
RATE_INTEREST_PRIMARY is highly overall correlated with FLAG_LAST_APPL_PER_CONTRACT and 7 other fieldsHigh correlation
RATE_INTEREST_PRIVILEGED is highly overall correlated with CHANNEL_TYPE and 14 other fieldsHigh correlation
FLAG_LAST_APPL_PER_CONTRACT is highly imbalanced (95.4%) Imbalance
NFLAG_LAST_APPL_IN_DAY is highly imbalanced (96.6%) Imbalance
NAME_CASH_LOAN_PURPOSE is highly imbalanced (71.5%) Imbalance
CODE_REJECT_REASON is highly imbalanced (66.2%) Imbalance
NAME_GOODS_CATEGORY is highly imbalanced (52.9%) Imbalance
AMT_ANNUITY has 372235 (22.3%) missing values Missing
AMT_DOWN_PAYMENT has 895844 (53.6%) missing values Missing
AMT_GOODS_PRICE has 385515 (23.1%) missing values Missing
RATE_DOWN_PAYMENT has 895844 (53.6%) missing values Missing
RATE_INTEREST_PRIMARY has 1664263 (99.6%) missing values Missing
RATE_INTEREST_PRIVILEGED has 1664263 (99.6%) missing values Missing
NAME_TYPE_SUITE has 820405 (49.1%) missing values Missing
CNT_PAYMENT has 372230 (22.3%) missing values Missing
DAYS_FIRST_DRAWING has 673065 (40.3%) missing values Missing
DAYS_FIRST_DUE has 673065 (40.3%) missing values Missing
DAYS_LAST_DUE_1ST_VERSION has 673065 (40.3%) missing values Missing
DAYS_LAST_DUE has 673065 (40.3%) missing values Missing
DAYS_TERMINATION has 673065 (40.3%) missing values Missing
NFLAG_INSURED_ON_APPROVAL has 673065 (40.3%) missing values Missing
AMT_DOWN_PAYMENT is highly skewed (γ1 = 36.47657581) Skewed
SELLERPLACE_AREA is highly skewed (γ1 = 529.6202788) Skewed
SK_ID_PREV is uniformly distributed Uniform
SK_ID_PREV has unique values Unique
AMT_APPLICATION has 392402 (23.5%) zeros Zeros
AMT_CREDIT has 336768 (20.2%) zeros Zeros
AMT_DOWN_PAYMENT has 369854 (22.1%) zeros Zeros
RATE_DOWN_PAYMENT has 369854 (22.1%) zeros Zeros
SELLERPLACE_AREA has 60523 (3.6%) zeros Zeros
CNT_PAYMENT has 144985 (8.7%) zeros Zeros

Reproduction

Analysis started2025-02-02 09:12:54.210496
Analysis finished2025-02-02 09:22:21.134692
Duration9 minutes and 26.92 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

SK_ID_PREV
Real number (ℝ)

Uniform  Unique 

Distinct1670214
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1923089.1
Minimum1000001
Maximum2845382
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 MiB
2025-02-02T14:52:21.309475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1000001
5-th percentile1092565.3
Q11461857.2
median1923110.5
Q32384279.8
95-th percentile2753178.3
Maximum2845382
Range1845381
Interquartile range (IQR)922422.5

Descriptive statistics

Standard deviation532597.96
Coefficient of variation (CV)0.27694918
Kurtosis-1.1997524
Mean1923089.1
Median Absolute Deviation (MAD)461211.5
Skewness-0.00057313346
Sum3.2119704 × 1012
Variance2.8366059 × 1011
MonotonicityNot monotonic
2025-02-02T14:52:21.452110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2030495 1
 
< 0.1%
1035848 1
 
< 0.1%
1526498 1
 
< 0.1%
2148893 1
 
< 0.1%
2437429 1
 
< 0.1%
1624541 1
 
< 0.1%
2095602 1
 
< 0.1%
1203077 1
 
< 0.1%
2842426 1
 
< 0.1%
1758596 1
 
< 0.1%
Other values (1670204) 1670204
> 99.9%
ValueCountFrequency (%)
1000001 1
< 0.1%
1000002 1
< 0.1%
1000003 1
< 0.1%
1000004 1
< 0.1%
1000005 1
< 0.1%
1000006 1
< 0.1%
1000007 1
< 0.1%
1000008 1
< 0.1%
1000009 1
< 0.1%
1000010 1
< 0.1%
ValueCountFrequency (%)
2845382 1
< 0.1%
2845381 1
< 0.1%
2845379 1
< 0.1%
2845378 1
< 0.1%
2845377 1
< 0.1%
2845373 1
< 0.1%
2845372 1
< 0.1%
2845370 1
< 0.1%
2845369 1
< 0.1%
2845368 1
< 0.1%

SK_ID_CURR
Real number (ℝ)

Distinct338857
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean278357.17
Minimum100001
Maximum456255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 MiB
2025-02-02T14:52:21.626823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100001
5-th percentile117930
Q1189329
median278714.5
Q3367514
95-th percentile438443
Maximum456255
Range356254
Interquartile range (IQR)178185

Descriptive statistics

Standard deviation102814.82
Coefficient of variation (CV)0.36936294
Kurtosis-1.199259
Mean278357.17
Median Absolute Deviation (MAD)89120.5
Skewness-0.0033025438
Sum4.6491605 × 1011
Variance1.0570888 × 1010
MonotonicityNot monotonic
2025-02-02T14:52:21.864542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
187868 77
 
< 0.1%
265681 73
 
< 0.1%
173680 72
 
< 0.1%
242412 68
 
< 0.1%
206783 67
 
< 0.1%
156367 66
 
< 0.1%
389950 64
 
< 0.1%
382179 64
 
< 0.1%
198355 63
 
< 0.1%
345161 62
 
< 0.1%
Other values (338847) 1669538
> 99.9%
ValueCountFrequency (%)
100001 1
 
< 0.1%
100002 1
 
< 0.1%
100003 3
 
< 0.1%
100004 1
 
< 0.1%
100005 2
 
< 0.1%
100006 9
< 0.1%
100007 6
< 0.1%
100008 5
< 0.1%
100009 7
< 0.1%
100010 1
 
< 0.1%
ValueCountFrequency (%)
456255 8
< 0.1%
456254 2
 
< 0.1%
456253 2
 
< 0.1%
456252 1
 
< 0.1%
456251 1
 
< 0.1%
456250 8
< 0.1%
456249 2
 
< 0.1%
456248 4
< 0.1%
456247 5
< 0.1%
456246 2
 
< 0.1%

NAME_CONTRACT_TYPE
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size110.4 MiB
Cash loans
747553 
Consumer loans
729151 
Revolving loans
193164 
XNA
 
346

Length

Max length15
Median length14
Mean length12.323057
Min length3

Characters and Unicode

Total characters20582142
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConsumer loans
2nd rowCash loans
3rd rowCash loans
4th rowCash loans
5th rowCash loans

Common Values

ValueCountFrequency (%)
Cash loans 747553
44.8%
Consumer loans 729151
43.7%
Revolving loans 193164
 
11.6%
XNA 346
 
< 0.1%

Length

2025-02-02T14:52:22.152381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-02T14:52:22.354687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
loans 1669868
50.0%
cash 747553
22.4%
consumer 729151
21.8%
revolving 193164
 
5.8%
xna 346
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
s 3146572
15.3%
o 2592183
12.6%
n 2592183
12.6%
a 2417421
11.7%
l 1863032
9.1%
1669868
8.1%
C 1476704
7.2%
e 922315
 
4.5%
h 747553
 
3.6%
r 729151
 
3.5%
Other values (9) 2425160
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20582142
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 3146572
15.3%
o 2592183
12.6%
n 2592183
12.6%
a 2417421
11.7%
l 1863032
9.1%
1669868
8.1%
C 1476704
7.2%
e 922315
 
4.5%
h 747553
 
3.6%
r 729151
 
3.5%
Other values (9) 2425160
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20582142
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 3146572
15.3%
o 2592183
12.6%
n 2592183
12.6%
a 2417421
11.7%
l 1863032
9.1%
1669868
8.1%
C 1476704
7.2%
e 922315
 
4.5%
h 747553
 
3.6%
r 729151
 
3.5%
Other values (9) 2425160
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20582142
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 3146572
15.3%
o 2592183
12.6%
n 2592183
12.6%
a 2417421
11.7%
l 1863032
9.1%
1669868
8.1%
C 1476704
7.2%
e 922315
 
4.5%
h 747553
 
3.6%
r 729151
 
3.5%
Other values (9) 2425160
11.8%

AMT_ANNUITY
Real number (ℝ)

High correlation  Missing 

Distinct357959
Distinct (%)27.6%
Missing372235
Missing (%)22.3%
Infinite0
Infinite (%)0.0%
Mean15955.121
Minimum0
Maximum418058.15
Zeros1637
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.7 MiB
2025-02-02T14:52:22.666053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2726.595
Q16321.78
median11250
Q320658.42
95-th percentile45336.78
Maximum418058.15
Range418058.15
Interquartile range (IQR)14336.64

Descriptive statistics

Standard deviation14782.137
Coefficient of variation (CV)0.92648233
Kurtosis15.069832
Mean15955.121
Median Absolute Deviation (MAD)5979.195
Skewness2.6925715
Sum2.0709412 × 1010
Variance2.1851158 × 108
MonotonicityNot monotonic
2025-02-02T14:52:22.960303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2250 31865
 
1.9%
11250 13974
 
0.8%
6750 13442
 
0.8%
9000 12496
 
0.7%
22500 11903
 
0.7%
4500 10597
 
0.6%
13500 7171
 
0.4%
3375 4806
 
0.3%
7875 4674
 
0.3%
38250 4129
 
0.2%
Other values (357949) 1182922
70.8%
(Missing) 372235
 
22.3%
ValueCountFrequency (%)
0 1637
0.1%
579.78 1
 
< 0.1%
585.855 1
 
< 0.1%
635.04 1
 
< 0.1%
637.65 1
 
< 0.1%
643.59 1
 
< 0.1%
646.515 2
 
< 0.1%
656.73 1
 
< 0.1%
665.19 1
 
< 0.1%
672.975 1
 
< 0.1%
ValueCountFrequency (%)
418058.145 2
< 0.1%
417927.645 2
< 0.1%
393868.665 1
< 0.1%
357733.26 1
< 0.1%
309942 1
< 0.1%
300425.445 1
< 0.1%
298557.585 2
< 0.1%
298427.085 2
< 0.1%
290358 1
< 0.1%
281027.25 1
< 0.1%

AMT_APPLICATION
Real number (ℝ)

High correlation  Zeros 

Distinct93885
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175233.86
Minimum0
Maximum6905160
Zeros392402
Zeros (%)23.5%
Negative0
Negative (%)0.0%
Memory size12.7 MiB
2025-02-02T14:52:23.258090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118720
median71046
Q3180360
95-th percentile787500
Maximum6905160
Range6905160
Interquartile range (IQR)161640

Descriptive statistics

Standard deviation292779.76
Coefficient of variation (CV)1.6707945
Kurtosis15.762243
Mean175233.86
Median Absolute Deviation (MAD)71046
Skewness3.3914422
Sum2.9267805 × 1011
Variance8.5719989 × 1010
MonotonicityNot monotonic
2025-02-02T14:52:24.262403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 392402
 
23.5%
45000 47831
 
2.9%
225000 43543
 
2.6%
135000 40678
 
2.4%
450000 38905
 
2.3%
90000 29367
 
1.8%
180000 24738
 
1.5%
270000 20573
 
1.2%
675000 20227
 
1.2%
67500 16861
 
1.0%
Other values (93875) 995089
59.6%
ValueCountFrequency (%)
0 392402
23.5%
3456 1
 
< 0.1%
4225.5 1
 
< 0.1%
4500 4
 
< 0.1%
5400 1
 
< 0.1%
5535 1
 
< 0.1%
5575.5 1
 
< 0.1%
5580 1
 
< 0.1%
5710.5 1
 
< 0.1%
5715 1
 
< 0.1%
ValueCountFrequency (%)
6905160 1
 
< 0.1%
5850000 2
 
< 0.1%
5085000 1
 
< 0.1%
4455000 1
 
< 0.1%
4237875 3
 
< 0.1%
4185000 1
 
< 0.1%
4140000 1
 
< 0.1%
4050000 12
< 0.1%
4005000 1
 
< 0.1%
3982500 1
 
< 0.1%

AMT_CREDIT
Real number (ℝ)

High correlation  Zeros 

Distinct86803
Distinct (%)5.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean196114.02
Minimum0
Maximum6905160
Zeros336768
Zeros (%)20.2%
Negative0
Negative (%)0.0%
Memory size12.7 MiB
2025-02-02T14:52:24.562204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q124160.5
median80541
Q3216418.5
95-th percentile886500
Maximum6905160
Range6905160
Interquartile range (IQR)192258

Descriptive statistics

Standard deviation318574.62
Coefficient of variation (CV)1.6244357
Kurtosis14.238793
Mean196114.02
Median Absolute Deviation (MAD)80541
Skewness3.2458146
Sum3.2755219 × 1011
Variance1.0148979 × 1011
MonotonicityNot monotonic
2025-02-02T14:52:24.896216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 336768
 
20.2%
45000 35051
 
2.1%
225000 21094
 
1.3%
450000 19954
 
1.2%
135000 18720
 
1.1%
180000 17085
 
1.0%
90000 13781
 
0.8%
270000 9842
 
0.6%
900000 7432
 
0.4%
67500 7245
 
0.4%
Other values (86793) 1183241
70.8%
ValueCountFrequency (%)
0 336768
20.2%
3456 1
 
< 0.1%
4225.5 1
 
< 0.1%
4500 4
 
< 0.1%
5139 1
 
< 0.1%
5143.5 1
 
< 0.1%
5179.5 1
 
< 0.1%
5355 1
 
< 0.1%
5562 1
 
< 0.1%
5571 1
 
< 0.1%
ValueCountFrequency (%)
6905160 1
 
< 0.1%
4509688.5 1
 
< 0.1%
4104351 4
 
< 0.1%
4095000 1
 
< 0.1%
4085550 1
 
< 0.1%
4050000 12
< 0.1%
4045711.5 1
 
< 0.1%
4009500 1
 
< 0.1%
4005000 1
 
< 0.1%
3847104 3
 
< 0.1%

AMT_DOWN_PAYMENT
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct29278
Distinct (%)3.8%
Missing895844
Missing (%)53.6%
Infinite0
Infinite (%)0.0%
Mean6697.4021
Minimum-0.9
Maximum3060045
Zeros369854
Zeros (%)22.1%
Negative2
Negative (%)< 0.1%
Memory size12.7 MiB
2025-02-02T14:52:25.165517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.9
5-th percentile0
Q10
median1638
Q37740
95-th percentile26184.082
Maximum3060045
Range3060045.9
Interquartile range (IQR)7740

Descriptive statistics

Standard deviation20921.495
Coefficient of variation (CV)3.1238225
Kurtosis2901.845
Mean6697.4021
Median Absolute Deviation (MAD)1638
Skewness36.476576
Sum5.1862673 × 109
Variance4.3770897 × 108
MonotonicityNot monotonic
2025-02-02T14:52:25.291516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 369854
22.1%
4500 21241
 
1.3%
9000 14747
 
0.9%
13500 9655
 
0.6%
22500 8165
 
0.5%
6750 7709
 
0.5%
2250 6241
 
0.4%
18000 4526
 
0.3%
45000 4059
 
0.2%
2700 3362
 
0.2%
Other values (29268) 324811
 
19.4%
(Missing) 895844
53.6%
ValueCountFrequency (%)
-0.9 1
 
< 0.1%
-0.45 1
 
< 0.1%
0 369854
22.1%
0.045 37
 
< 0.1%
0.09 40
 
< 0.1%
0.135 28
 
< 0.1%
0.18 39
 
< 0.1%
0.225 54
 
< 0.1%
0.27 36
 
< 0.1%
0.315 18
 
< 0.1%
ValueCountFrequency (%)
3060045 1
 
< 0.1%
2475000 1
 
< 0.1%
2150100 1
 
< 0.1%
2135700 1
 
< 0.1%
2118937.5 3
< 0.1%
2034000 1
 
< 0.1%
2025000 1
 
< 0.1%
1980000 2
< 0.1%
1964970 1
 
< 0.1%
1800000 2
< 0.1%

AMT_GOODS_PRICE
Real number (ℝ)

High correlation  Missing 

Distinct93885
Distinct (%)7.3%
Missing385515
Missing (%)23.1%
Infinite0
Infinite (%)0.0%
Mean227847.28
Minimum0
Maximum6905160
Zeros6869
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size12.7 MiB
2025-02-02T14:52:25.439016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22882.5
Q150841
median112320
Q3234000
95-th percentile900000
Maximum6905160
Range6905160
Interquartile range (IQR)183159

Descriptive statistics

Standard deviation315396.56
Coefficient of variation (CV)1.3842454
Kurtosis12.86636
Mean227847.28
Median Absolute Deviation (MAD)69030
Skewness3.0736897
Sum2.9271517 × 1011
Variance9.9474989 × 1010
MonotonicityNot monotonic
2025-02-02T14:52:25.574128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45000 47831
 
2.9%
225000 43549
 
2.6%
135000 40666
 
2.4%
450000 38926
 
2.3%
90000 29367
 
1.8%
180000 24736
 
1.5%
270000 20567
 
1.2%
675000 20235
 
1.2%
67500 16857
 
1.0%
900000 15572
 
0.9%
Other values (93875) 986393
59.1%
(Missing) 385515
 
23.1%
ValueCountFrequency (%)
0 6869
0.4%
3456 1
 
< 0.1%
4225.5 1
 
< 0.1%
4500 4
 
< 0.1%
5400 1
 
< 0.1%
5535 1
 
< 0.1%
5575.5 1
 
< 0.1%
5580 1
 
< 0.1%
5710.5 1
 
< 0.1%
5715 1
 
< 0.1%
ValueCountFrequency (%)
6905160 1
 
< 0.1%
5850000 2
 
< 0.1%
5085000 1
 
< 0.1%
4455000 1
 
< 0.1%
4237875 3
 
< 0.1%
4185000 1
 
< 0.1%
4140000 1
 
< 0.1%
4050000 12
< 0.1%
4005000 1
 
< 0.1%
3982500 1
 
< 0.1%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size102.3 MiB
TUESDAY
255118 
WEDNESDAY
255010 
MONDAY
253557 
FRIDAY
252048 
THURSDAY
249099 
Other values (2)
405382 

Length

Max length9
Median length8
Mean length7.1972166
Min length6

Characters and Unicode

Total characters12020892
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSATURDAY
2nd rowTHURSDAY
3rd rowTUESDAY
4th rowMONDAY
5th rowTHURSDAY

Common Values

ValueCountFrequency (%)
TUESDAY 255118
15.3%
WEDNESDAY 255010
15.3%
MONDAY 253557
15.2%
FRIDAY 252048
15.1%
THURSDAY 249099
14.9%
SATURDAY 240631
14.4%
SUNDAY 164751
9.9%

Length

2025-02-02T14:52:25.699948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-02T14:52:25.794548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
tuesday 255118
15.3%
wednesday 255010
15.3%
monday 253557
15.2%
friday 252048
15.1%
thursday 249099
14.9%
saturday 240631
14.4%
sunday 164751
9.9%

Most occurring characters

ValueCountFrequency (%)
D 1925224
16.0%
A 1910845
15.9%
Y 1670214
13.9%
S 1164609
9.7%
U 909599
7.6%
E 765138
 
6.4%
T 744848
 
6.2%
R 741778
 
6.2%
N 673318
 
5.6%
W 255010
 
2.1%
Other values (5) 1260309
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12020892
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 1925224
16.0%
A 1910845
15.9%
Y 1670214
13.9%
S 1164609
9.7%
U 909599
7.6%
E 765138
 
6.4%
T 744848
 
6.2%
R 741778
 
6.2%
N 673318
 
5.6%
W 255010
 
2.1%
Other values (5) 1260309
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12020892
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 1925224
16.0%
A 1910845
15.9%
Y 1670214
13.9%
S 1164609
9.7%
U 909599
7.6%
E 765138
 
6.4%
T 744848
 
6.2%
R 741778
 
6.2%
N 673318
 
5.6%
W 255010
 
2.1%
Other values (5) 1260309
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12020892
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 1925224
16.0%
A 1910845
15.9%
Y 1670214
13.9%
S 1164609
9.7%
U 909599
7.6%
E 765138
 
6.4%
T 744848
 
6.2%
R 741778
 
6.2%
N 673318
 
5.6%
W 255010
 
2.1%
Other values (5) 1260309
10.5%

HOUR_APPR_PROCESS_START
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.484182
Minimum0
Maximum23
Zeros109
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size12.7 MiB
2025-02-02T14:52:25.941611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q110
median12
Q315
95-th percentile18
Maximum23
Range23
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.334028
Coefficient of variation (CV)0.26706019
Kurtosis-0.27776836
Mean12.484182
Median Absolute Deviation (MAD)2
Skewness-0.025629249
Sum20851255
Variance11.115742
MonotonicityNot monotonic
2025-02-02T14:52:26.032686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
11 192728
11.5%
12 185980
11.1%
10 181690
10.9%
13 172256
10.3%
14 157711
9.4%
15 142965
8.6%
9 127002
7.6%
16 121361
7.3%
17 95064
5.7%
8 73085
 
4.4%
Other values (14) 220372
13.2%
ValueCountFrequency (%)
0 109
 
< 0.1%
1 212
 
< 0.1%
2 1116
 
0.1%
3 5035
 
0.3%
4 9319
 
0.6%
5 15392
 
0.9%
6 25759
 
1.5%
7 45646
 
2.7%
8 73085
4.4%
9 127002
7.6%
ValueCountFrequency (%)
23 202
 
< 0.1%
22 720
 
< 0.1%
21 4082
 
0.2%
20 14535
 
0.9%
19 34089
 
2.0%
18 64156
3.8%
17 95064
5.7%
16 121361
7.3%
15 142965
8.6%
14 157711
9.4%

FLAG_LAST_APPL_PER_CONTRACT
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
True
1661739 
False
 
8475
ValueCountFrequency (%)
True 1661739
99.5%
False 8475
 
0.5%
2025-02-02T14:52:26.111253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

NFLAG_LAST_APPL_IN_DAY
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size92.4 MiB
1
1664314 
0
 
5900

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1670214
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1664314
99.6%
0 5900
 
0.4%

Length

2025-02-02T14:52:26.174711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-02T14:52:26.238194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1664314
99.6%
0 5900
 
0.4%

Most occurring characters

ValueCountFrequency (%)
1 1664314
99.6%
0 5900
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1670214
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1664314
99.6%
0 5900
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1670214
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1664314
99.6%
0 5900
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1670214
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1664314
99.6%
0 5900
 
0.4%

RATE_DOWN_PAYMENT
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct207033
Distinct (%)26.7%
Missing895844
Missing (%)53.6%
Infinite0
Infinite (%)0.0%
Mean0.079636815
Minimum-1.4978763 × 10-5
Maximum1
Zeros369854
Zeros (%)22.1%
Negative2
Negative (%)< 0.1%
Memory size12.7 MiB
2025-02-02T14:52:26.444031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.4978763 × 10-5
5-th percentile0
Q10
median0.051605085
Q30.10890909
95-th percentile0.29412643
Maximum1
Range1.000015
Interquartile range (IQR)0.10890909

Descriptive statistics

Standard deviation0.10782331
Coefficient of variation (CV)1.353938
Kurtosis6.2044689
Mean0.079636815
Median Absolute Deviation (MAD)0.051605085
Skewness2.1077129
Sum61668.361
Variance0.011625867
MonotonicityNot monotonic
2025-02-02T14:52:26.763325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 369854
22.1%
0.1089090909 36341
 
2.2%
0.2178181818 6482
 
0.4%
0.3267272727 1081
 
0.1%
0.5445454545 746
 
< 0.1%
0.4356363636 449
 
< 0.1%
0.1042746615 304
 
< 0.1%
0.1013781431 258
 
< 0.1%
0.09946035699 252
 
< 0.1%
0.1000083479 243
 
< 0.1%
Other values (207023) 358360
21.5%
(Missing) 895844
53.6%
ValueCountFrequency (%)
-1.497876341 × 10-51
 
< 0.1%
-1.369340042 × 10-51
 
< 0.1%
0 369854
22.1%
1.554444933 × 10-71
 
< 0.1%
2.102938266 × 10-71
 
< 0.1%
2.168812116 × 10-71
 
< 0.1%
2.36993259 × 10-71
 
< 0.1%
2.397769026 × 10-71
 
< 0.1%
2.548534315 × 10-71
 
< 0.1%
2.723407444 × 10-71
 
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9897398776 1
< 0.1%
0.9807152645 1
< 0.1%
0.9801818182 1
< 0.1%
0.972550305 1
< 0.1%
0.9603411696 1
< 0.1%
0.9588557385 1
< 0.1%
0.9484449299 1
< 0.1%
0.9474932994 1
< 0.1%
0.9447764506 1
< 0.1%

RATE_INTEREST_PRIMARY
Real number (ℝ)

High correlation  Missing 

Distinct148
Distinct (%)2.5%
Missing1664263
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean0.18835689
Minimum0.034781254
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 MiB
2025-02-02T14:52:27.093948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.034781254
5-th percentile0.14244021
Q10.16071631
median0.18912218
Q30.19332993
95-th percentile0.19691431
Maximum1
Range0.96521875
Interquartile range (IQR)0.032613623

Descriptive statistics

Standard deviation0.087671105
Coefficient of variation (CV)0.46545207
Kurtosis28.204535
Mean0.18835689
Median Absolute Deviation (MAD)0.0077779667
Skewness5.198204
Sum1120.9118
Variance0.0076862226
MonotonicityNot monotonic
2025-02-02T14:52:27.394227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1891363482 1218
 
0.1%
0.1424402131 951
 
0.1%
0.1607163096 821
 
< 0.1%
0.1933299331 681
 
< 0.1%
0.1969001473 573
 
< 0.1%
0.1760030602 241
 
< 0.1%
0.1891221807 210
 
< 0.1%
0.1607021421 204
 
< 0.1%
0.1828176357 187
 
< 0.1%
0.1969143149 139
 
< 0.1%
Other values (138) 726
 
< 0.1%
(Missing) 1664263
99.6%
ValueCountFrequency (%)
0.03478125354 1
 
< 0.1%
0.05912104726 2
 
< 0.1%
0.05913521478 61
< 0.1%
0.0591493823 2
 
< 0.1%
0.09577241301 2
 
< 0.1%
0.1037345574 2
 
< 0.1%
0.1154794288 1
 
< 0.1%
0.1209622577 1
 
< 0.1%
0.1276351581 1
 
< 0.1%
0.1278335033 17
 
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9029241755 1
< 0.1%
0.8613000113 1
< 0.1%
0.8155105973 1
< 0.1%
0.8067692395 1
< 0.1%
0.7974470135 1
< 0.1%
0.7741981186 1
< 0.1%
0.7449846991 1
< 0.1%
0.7435821149 1
< 0.1%
0.7399977332 1
< 0.1%

RATE_INTEREST_PRIVILEGED
Real number (ℝ)

High correlation  Missing 

Distinct25
Distinct (%)0.4%
Missing1664263
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean0.77350254
Minimum0.37315011
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 MiB
2025-02-02T14:52:27.637136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.37315011
5-th percentile0.63794926
Q10.71564482
median0.83509514
Q30.852537
95-th percentile0.86733615
Maximum1
Range0.62684989
Interquartile range (IQR)0.13689218

Descriptive statistics

Standard deviation0.10087859
Coefficient of variation (CV)0.13041792
Kurtosis0.25557607
Mean0.77350254
Median Absolute Deviation (MAD)0.032241015
Skewness-1.00768
Sum4603.1136
Variance0.01017649
MonotonicityNot monotonic
2025-02-02T14:52:27.917278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.8350951374 1717
 
0.1%
0.7156448203 1046
 
0.1%
0.63794926 1039
 
0.1%
0.8673361522 931
 
0.1%
0.8525369979 876
 
0.1%
0.5687103594 127
 
< 0.1%
0.4244186047 66
 
< 0.1%
0.5137420719 45
 
< 0.1%
0.8324524313 40
 
< 0.1%
0.8451374207 19
 
< 0.1%
Other values (15) 45
 
< 0.1%
(Missing) 1664263
99.6%
ValueCountFrequency (%)
0.3731501057 2
 
< 0.1%
0.4244186047 66
< 0.1%
0.4365750529 2
 
< 0.1%
0.4841437632 1
 
< 0.1%
0.5021141649 1
 
< 0.1%
0.5137420719 45
 
< 0.1%
0.5428118393 1
 
< 0.1%
0.5480972516 1
 
< 0.1%
0.5687103594 127
< 0.1%
0.6374207188 7
 
< 0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.8673361522 931
0.1%
0.8546511628 1
 
< 0.1%
0.8525369979 876
0.1%
0.8451374207 19
 
< 0.1%
0.8350951374 1717
0.1%
0.8324524313 40
 
< 0.1%
0.8208245243 1
 
< 0.1%
0.8065539112 4
 
< 0.1%
0.7906976744 5
 
< 0.1%

NAME_CASH_LOAN_PURPOSE
Categorical

High correlation  Imbalance 

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.1 MiB
XAP
922661 
XNA
677918 
Repairs
 
23765
Other
 
15608
Urgent needs
 
8412
Other values (20)
 
21850

Length

Max length32
Median length3
Mean length3.3122025
Min length3

Characters and Unicode

Total characters5532087
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowXAP
2nd rowXNA
3rd rowXNA
4th rowXNA
5th rowRepairs

Common Values

ValueCountFrequency (%)
XAP 922661
55.2%
XNA 677918
40.6%
Repairs 23765
 
1.4%
Other 15608
 
0.9%
Urgent needs 8412
 
0.5%
Buying a used car 2888
 
0.2%
Building a house or an annex 2693
 
0.2%
Everyday expenses 2416
 
0.1%
Medicine 2174
 
0.1%
Payments on other loans 1931
 
0.1%
Other values (15) 9748
 
0.6%

Length

2025-02-02T14:52:28.442188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
xap 922661
53.5%
xna 677918
39.3%
repairs 24562
 
1.4%
other 17539
 
1.0%
urgent 8412
 
0.5%
needs 8412
 
0.5%
a 8152
 
0.5%
buying 5434
 
0.3%
car 4697
 
0.3%
used 2888
 
0.2%
Other values (41) 45306
 
2.6%

Most occurring characters

ValueCountFrequency (%)
X 1600579
28.9%
A 1600579
28.9%
P 925653
16.7%
N 677918
12.3%
e 104454
 
1.9%
r 66486
 
1.2%
55767
 
1.0%
a 54954
 
1.0%
n 52820
 
1.0%
s 50228
 
0.9%
Other values (31) 342649
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5532087
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
X 1600579
28.9%
A 1600579
28.9%
P 925653
16.7%
N 677918
12.3%
e 104454
 
1.9%
r 66486
 
1.2%
55767
 
1.0%
a 54954
 
1.0%
n 52820
 
1.0%
s 50228
 
0.9%
Other values (31) 342649
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5532087
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
X 1600579
28.9%
A 1600579
28.9%
P 925653
16.7%
N 677918
12.3%
e 104454
 
1.9%
r 66486
 
1.2%
55767
 
1.0%
a 54954
 
1.0%
n 52820
 
1.0%
s 50228
 
0.9%
Other values (31) 342649
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5532087
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
X 1600579
28.9%
A 1600579
28.9%
P 925653
16.7%
N 677918
12.3%
e 104454
 
1.9%
r 66486
 
1.2%
55767
 
1.0%
a 54954
 
1.0%
n 52820
 
1.0%
s 50228
 
0.9%
Other values (31) 342649
 
6.2%

NAME_CONTRACT_STATUS
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size103.4 MiB
Approved
1036781 
Canceled
316319 
Refused
290678 
Unused offer
 
26436

Length

Max length12
Median length8
Mean length7.8892753
Min length7

Characters and Unicode

Total characters13176778
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApproved
2nd rowApproved
3rd rowApproved
4th rowApproved
5th rowRefused

Common Values

ValueCountFrequency (%)
Approved 1036781
62.1%
Canceled 316319
 
18.9%
Refused 290678
 
17.4%
Unused offer 26436
 
1.6%

Length

2025-02-02T14:52:28.694725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-02T14:52:28.868429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
approved 1036781
61.1%
canceled 316319
 
18.6%
refused 290678
 
17.1%
unused 26436
 
1.6%
offer 26436
 
1.6%

Most occurring characters

ValueCountFrequency (%)
e 2303647
17.5%
p 2073562
15.7%
d 1670214
12.7%
r 1063217
8.1%
o 1063217
8.1%
A 1036781
7.9%
v 1036781
7.9%
f 343550
 
2.6%
n 342755
 
2.6%
u 317114
 
2.4%
Other values (8) 1925940
14.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13176778
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2303647
17.5%
p 2073562
15.7%
d 1670214
12.7%
r 1063217
8.1%
o 1063217
8.1%
A 1036781
7.9%
v 1036781
7.9%
f 343550
 
2.6%
n 342755
 
2.6%
u 317114
 
2.4%
Other values (8) 1925940
14.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13176778
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2303647
17.5%
p 2073562
15.7%
d 1670214
12.7%
r 1063217
8.1%
o 1063217
8.1%
A 1036781
7.9%
v 1036781
7.9%
f 343550
 
2.6%
n 342755
 
2.6%
u 317114
 
2.4%
Other values (8) 1925940
14.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13176778
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2303647
17.5%
p 2073562
15.7%
d 1670214
12.7%
r 1063217
8.1%
o 1063217
8.1%
A 1036781
7.9%
v 1036781
7.9%
f 343550
 
2.6%
n 342755
 
2.6%
u 317114
 
2.4%
Other values (8) 1925940
14.6%

DAYS_DECISION
Real number (ℝ)

High correlation 

Distinct2922
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-880.67967
Minimum-2922
Maximum-1
Zeros0
Zeros (%)0.0%
Negative1670214
Negative (%)100.0%
Memory size12.7 MiB
2025-02-02T14:52:29.145995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2922
5-th percentile-2559
Q1-1300
median-581
Q3-280
95-th percentile-85
Maximum-1
Range2921
Interquartile range (IQR)1020

Descriptive statistics

Standard deviation779.09967
Coefficient of variation (CV)-0.88465727
Kurtosis-0.037845835
Mean-880.67967
Median Absolute Deviation (MAD)375
Skewness-1.0530797
Sum-1.4709235 × 109
Variance606996.29
MonotonicityNot monotonic
2025-02-02T14:52:29.489021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-245 2444
 
0.1%
-238 2390
 
0.1%
-210 2375
 
0.1%
-273 2350
 
0.1%
-196 2315
 
0.1%
-224 2305
 
0.1%
-252 2300
 
0.1%
-182 2283
 
0.1%
-240 2279
 
0.1%
-231 2270
 
0.1%
Other values (2912) 1646903
98.6%
ValueCountFrequency (%)
-2922 162
< 0.1%
-2921 158
< 0.1%
-2920 168
< 0.1%
-2919 171
< 0.1%
-2918 185
< 0.1%
-2917 174
< 0.1%
-2916 166
< 0.1%
-2915 169
< 0.1%
-2914 192
< 0.1%
-2913 188
< 0.1%
ValueCountFrequency (%)
-1 2
 
< 0.1%
-2 1172
0.1%
-3 1516
0.1%
-4 1507
0.1%
-5 1324
0.1%
-6 1363
0.1%
-7 1697
0.1%
-8 1399
0.1%
-9 1228
0.1%
-10 1138
0.1%

NAME_PAYMENT_TYPE
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size113.5 MiB
Cash through the bank
1033552 
XNA
627384 
Non-cash from your account
 
8193
Cashless from the account of the employer
 
1085

Length

Max length41
Median length21
Mean length14.276163
Min length3

Characters and Unicode

Total characters23844247
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCash through the bank
2nd rowXNA
3rd rowCash through the bank
4th rowCash through the bank
5th rowCash through the bank

Common Values

ValueCountFrequency (%)
Cash through the bank 1033552
61.9%
XNA 627384
37.6%
Non-cash from your account 8193
 
0.5%
Cashless from the account of the employer 1085
 
0.1%

Length

2025-02-02T14:52:29.724867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-02T14:52:29.820212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
the 1035722
21.6%
cash 1033552
21.5%
through 1033552
21.5%
bank 1033552
21.5%
xna 627384
13.1%
from 9278
 
0.2%
account 9278
 
0.2%
non-cash 8193
 
0.2%
your 8193
 
0.2%
cashless 1085
 
< 0.1%
Other values (2) 2170
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
h 4145656
17.4%
3131745
13.1%
a 2085660
 
8.7%
t 2078552
 
8.7%
o 1070664
 
4.5%
r 1052108
 
4.4%
n 1051023
 
4.4%
u 1051023
 
4.4%
s 1045000
 
4.4%
e 1038977
 
4.4%
Other values (14) 6093839
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23844247
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
h 4145656
17.4%
3131745
13.1%
a 2085660
 
8.7%
t 2078552
 
8.7%
o 1070664
 
4.5%
r 1052108
 
4.4%
n 1051023
 
4.4%
u 1051023
 
4.4%
s 1045000
 
4.4%
e 1038977
 
4.4%
Other values (14) 6093839
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23844247
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
h 4145656
17.4%
3131745
13.1%
a 2085660
 
8.7%
t 2078552
 
8.7%
o 1070664
 
4.5%
r 1052108
 
4.4%
n 1051023
 
4.4%
u 1051023
 
4.4%
s 1045000
 
4.4%
e 1038977
 
4.4%
Other values (14) 6093839
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23844247
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
h 4145656
17.4%
3131745
13.1%
a 2085660
 
8.7%
t 2078552
 
8.7%
o 1070664
 
4.5%
r 1052108
 
4.4%
n 1051023
 
4.4%
u 1051023
 
4.4%
s 1045000
 
4.4%
e 1038977
 
4.4%
Other values (14) 6093839
25.6%

CODE_REJECT_REASON
Categorical

High correlation  Imbalance 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size95.6 MiB
XAP
1353093 
HC
175231 
LIMIT
 
55680
SCO
 
37467
CLIENT
 
26436
Other values (4)
 
22307

Length

Max length6
Median length3
Mean length3.0301039
Min length2

Characters and Unicode

Total characters5060922
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowXAP
2nd rowXAP
3rd rowXAP
4th rowXAP
5th rowHC

Common Values

ValueCountFrequency (%)
XAP 1353093
81.0%
HC 175231
 
10.5%
LIMIT 55680
 
3.3%
SCO 37467
 
2.2%
CLIENT 26436
 
1.6%
SCOFR 12811
 
0.8%
XNA 5244
 
0.3%
VERIF 3535
 
0.2%
SYSTEM 717
 
< 0.1%

Length

2025-02-02T14:52:29.947056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-02T14:52:30.057631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
xap 1353093
81.0%
hc 175231
 
10.5%
limit 55680
 
3.3%
sco 37467
 
2.2%
client 26436
 
1.6%
scofr 12811
 
0.8%
xna 5244
 
0.3%
verif 3535
 
0.2%
system 717
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
X 1358337
26.8%
A 1358337
26.8%
P 1353093
26.7%
C 251945
 
5.0%
H 175231
 
3.5%
I 141331
 
2.8%
T 82833
 
1.6%
L 82116
 
1.6%
M 56397
 
1.1%
S 51712
 
1.0%
Other values (7) 149590
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5060922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
X 1358337
26.8%
A 1358337
26.8%
P 1353093
26.7%
C 251945
 
5.0%
H 175231
 
3.5%
I 141331
 
2.8%
T 82833
 
1.6%
L 82116
 
1.6%
M 56397
 
1.1%
S 51712
 
1.0%
Other values (7) 149590
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5060922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
X 1358337
26.8%
A 1358337
26.8%
P 1353093
26.7%
C 251945
 
5.0%
H 175231
 
3.5%
I 141331
 
2.8%
T 82833
 
1.6%
L 82116
 
1.6%
M 56397
 
1.1%
S 51712
 
1.0%
Other values (7) 149590
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5060922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
X 1358337
26.8%
A 1358337
26.8%
P 1353093
26.7%
C 251945
 
5.0%
H 175231
 
3.5%
I 141331
 
2.8%
T 82833
 
1.6%
L 82116
 
1.6%
M 56397
 
1.1%
S 51712
 
1.0%
Other values (7) 149590
 
3.0%

NAME_TYPE_SUITE
Categorical

Missing 

Distinct7
Distinct (%)< 0.1%
Missing820405
Missing (%)49.1%
Memory size99.0 MiB
Unaccompanied
508970 
Family
213263 
Spouse, partner
67069 
Children
 
31566
Other_B
 
17624
Other values (2)
 
11317

Length

Max length15
Median length13
Mean length11.032194
Min length6

Characters and Unicode

Total characters9375258
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnaccompanied
2nd rowSpouse, partner
3rd rowFamily
4th rowUnaccompanied
5th rowUnaccompanied

Common Values

ValueCountFrequency (%)
Unaccompanied 508970
30.5%
Family 213263
 
12.8%
Spouse, partner 67069
 
4.0%
Children 31566
 
1.9%
Other_B 17624
 
1.1%
Other_A 9077
 
0.5%
Group of people 2240
 
0.1%
(Missing) 820405
49.1%

Length

2025-02-02T14:52:30.184023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-02T14:52:30.278613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
unaccompanied 508970
55.2%
family 213263
23.1%
spouse 67069
 
7.3%
partner 67069
 
7.3%
children 31566
 
3.4%
other_b 17624
 
1.9%
other_a 9077
 
1.0%
group 2240
 
0.2%
of 2240
 
0.2%
people 2240
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a 1298272
13.8%
n 1116575
11.9%
c 1017940
10.9%
i 753799
8.0%
m 722233
7.7%
e 705855
7.5%
p 649828
6.9%
o 582759
 
6.2%
d 540536
 
5.8%
U 508970
 
5.4%
Other values (18) 1478491
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9375258
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1298272
13.8%
n 1116575
11.9%
c 1017940
10.9%
i 753799
8.0%
m 722233
7.7%
e 705855
7.5%
p 649828
6.9%
o 582759
 
6.2%
d 540536
 
5.8%
U 508970
 
5.4%
Other values (18) 1478491
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9375258
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1298272
13.8%
n 1116575
11.9%
c 1017940
10.9%
i 753799
8.0%
m 722233
7.7%
e 705855
7.5%
p 649828
6.9%
o 582759
 
6.2%
d 540536
 
5.8%
U 508970
 
5.4%
Other values (18) 1478491
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9375258
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1298272
13.8%
n 1116575
11.9%
c 1017940
10.9%
i 753799
8.0%
m 722233
7.7%
e 705855
7.5%
p 649828
6.9%
o 582759
 
6.2%
d 540536
 
5.8%
U 508970
 
5.4%
Other values (18) 1478491
15.8%

NAME_CLIENT_TYPE
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size102.2 MiB
Repeater
1231261 
New
301363 
Refreshed
135649 
XNA
 
1941

Length

Max length9
Median length8
Mean length7.1732371
Min length3

Characters and Unicode

Total characters11980841
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRepeater
2nd rowRepeater
3rd rowRepeater
4th rowRepeater
5th rowRepeater

Common Values

ValueCountFrequency (%)
Repeater 1231261
73.7%
New 301363
 
18.0%
Refreshed 135649
 
8.1%
XNA 1941
 
0.1%

Length

2025-02-02T14:52:30.420953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-02T14:52:30.516330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
repeater 1231261
73.7%
new 301363
 
18.0%
refreshed 135649
 
8.1%
xna 1941
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 4402093
36.7%
R 1366910
 
11.4%
r 1366910
 
11.4%
p 1231261
 
10.3%
a 1231261
 
10.3%
t 1231261
 
10.3%
N 303304
 
2.5%
w 301363
 
2.5%
f 135649
 
1.1%
s 135649
 
1.1%
Other values (4) 275180
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11980841
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4402093
36.7%
R 1366910
 
11.4%
r 1366910
 
11.4%
p 1231261
 
10.3%
a 1231261
 
10.3%
t 1231261
 
10.3%
N 303304
 
2.5%
w 301363
 
2.5%
f 135649
 
1.1%
s 135649
 
1.1%
Other values (4) 275180
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11980841
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4402093
36.7%
R 1366910
 
11.4%
r 1366910
 
11.4%
p 1231261
 
10.3%
a 1231261
 
10.3%
t 1231261
 
10.3%
N 303304
 
2.5%
w 301363
 
2.5%
f 135649
 
1.1%
s 135649
 
1.1%
Other values (4) 275180
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11980841
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4402093
36.7%
R 1366910
 
11.4%
r 1366910
 
11.4%
p 1231261
 
10.3%
a 1231261
 
10.3%
t 1231261
 
10.3%
N 303304
 
2.5%
w 301363
 
2.5%
f 135649
 
1.1%
s 135649
 
1.1%
Other values (4) 275180
 
2.3%

NAME_GOODS_CATEGORY
Categorical

High correlation  Imbalance 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.6 MiB
XNA
950809 
Mobile
224708 
Consumer Electronics
121576 
Computers
105769 
Audio/Video
99441 
Other values (23)
167911 

Length

Max length24
Median length3
Mean length6.786328
Min length3

Characters and Unicode

Total characters11334620
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowMobile
2nd rowXNA
3rd rowXNA
4th rowXNA
5th rowXNA

Common Values

ValueCountFrequency (%)
XNA 950809
56.9%
Mobile 224708
 
13.5%
Consumer Electronics 121576
 
7.3%
Computers 105769
 
6.3%
Audio/Video 99441
 
6.0%
Furniture 53656
 
3.2%
Photo / Cinema Equipment 25021
 
1.5%
Construction Materials 24995
 
1.5%
Clothing and Accessories 23554
 
1.4%
Auto Accessories 7381
 
0.4%
Other values (18) 33304
 
2.0%

Length

2025-02-02T14:52:30.626826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
xna 950809
48.5%
mobile 224708
 
11.5%
consumer 121576
 
6.2%
electronics 121576
 
6.2%
computers 105769
 
5.4%
audio/video 99441
 
5.1%
furniture 53656
 
2.7%
accessories 30935
 
1.6%
and 26535
 
1.4%
equipment 25021
 
1.3%
Other values (30) 199024
 
10.2%

Most occurring characters

ValueCountFrequency (%)
A 1091028
 
9.6%
X 950809
 
8.4%
N 950809
 
8.4%
o 944391
 
8.3%
e 921134
 
8.1%
i 780621
 
6.9%
r 561953
 
5.0%
s 511856
 
4.5%
u 500151
 
4.4%
n 456800
 
4.0%
Other values (33) 3665068
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11334620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1091028
 
9.6%
X 950809
 
8.4%
N 950809
 
8.4%
o 944391
 
8.3%
e 921134
 
8.1%
i 780621
 
6.9%
r 561953
 
5.0%
s 511856
 
4.5%
u 500151
 
4.4%
n 456800
 
4.0%
Other values (33) 3665068
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11334620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1091028
 
9.6%
X 950809
 
8.4%
N 950809
 
8.4%
o 944391
 
8.3%
e 921134
 
8.1%
i 780621
 
6.9%
r 561953
 
5.0%
s 511856
 
4.5%
u 500151
 
4.4%
n 456800
 
4.0%
Other values (33) 3665068
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11334620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1091028
 
9.6%
X 950809
 
8.4%
N 950809
 
8.4%
o 944391
 
8.3%
e 921134
 
8.1%
i 780621
 
6.9%
r 561953
 
5.0%
s 511856
 
4.5%
u 500151
 
4.4%
n 456800
 
4.0%
Other values (33) 3665068
32.3%

NAME_PORTFOLIO
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.3 MiB
POS
691011 
Cash
461563 
XNA
372230 
Cards
144985 
Cars
 
425

Length

Max length5
Median length3
Mean length3.4502166
Min length3

Characters and Unicode

Total characters5762600
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPOS
2nd rowCash
3rd rowCash
4th rowCash
5th rowCash

Common Values

ValueCountFrequency (%)
POS 691011
41.4%
Cash 461563
27.6%
XNA 372230
22.3%
Cards 144985
 
8.7%
Cars 425
 
< 0.1%

Length

2025-02-02T14:52:30.753070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-02T14:52:30.895966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
pos 691011
41.4%
cash 461563
27.6%
xna 372230
22.3%
cards 144985
 
8.7%
cars 425
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
P 691011
12.0%
O 691011
12.0%
S 691011
12.0%
C 606973
10.5%
a 606973
10.5%
s 606973
10.5%
h 461563
8.0%
X 372230
6.5%
N 372230
6.5%
A 372230
6.5%
Other values (2) 290395
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5762600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 691011
12.0%
O 691011
12.0%
S 691011
12.0%
C 606973
10.5%
a 606973
10.5%
s 606973
10.5%
h 461563
8.0%
X 372230
6.5%
N 372230
6.5%
A 372230
6.5%
Other values (2) 290395
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5762600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 691011
12.0%
O 691011
12.0%
S 691011
12.0%
C 606973
10.5%
a 606973
10.5%
s 606973
10.5%
h 461563
8.0%
X 372230
6.5%
N 372230
6.5%
A 372230
6.5%
Other values (2) 290395
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5762600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 691011
12.0%
O 691011
12.0%
S 691011
12.0%
C 606973
10.5%
a 606973
10.5%
s 606973
10.5%
h 461563
8.0%
X 372230
6.5%
N 372230
6.5%
A 372230
6.5%
Other values (2) 290395
5.0%

NAME_PRODUCT_TYPE
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size97.4 MiB
XNA
1063666 
x-sell
456287 
walk-in
150261 

Length

Max length7
Median length3
Mean length4.1794327
Min length3

Characters and Unicode

Total characters6980547
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowXNA
2nd rowx-sell
3rd rowx-sell
4th rowx-sell
5th rowwalk-in

Common Values

ValueCountFrequency (%)
XNA 1063666
63.7%
x-sell 456287
27.3%
walk-in 150261
 
9.0%

Length

2025-02-02T14:52:31.165427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-02T14:52:31.350431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
xna 1063666
63.7%
x-sell 456287
27.3%
walk-in 150261
 
9.0%

Most occurring characters

ValueCountFrequency (%)
X 1063666
15.2%
N 1063666
15.2%
A 1063666
15.2%
l 1062835
15.2%
- 606548
8.7%
x 456287
6.5%
s 456287
6.5%
e 456287
6.5%
w 150261
 
2.2%
a 150261
 
2.2%
Other values (3) 450783
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6980547
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
X 1063666
15.2%
N 1063666
15.2%
A 1063666
15.2%
l 1062835
15.2%
- 606548
8.7%
x 456287
6.5%
s 456287
6.5%
e 456287
6.5%
w 150261
 
2.2%
a 150261
 
2.2%
Other values (3) 450783
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6980547
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
X 1063666
15.2%
N 1063666
15.2%
A 1063666
15.2%
l 1062835
15.2%
- 606548
8.7%
x 456287
6.5%
s 456287
6.5%
e 456287
6.5%
w 150261
 
2.2%
a 150261
 
2.2%
Other values (3) 450783
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6980547
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
X 1063666
15.2%
N 1063666
15.2%
A 1063666
15.2%
l 1062835
15.2%
- 606548
8.7%
x 456287
6.5%
s 456287
6.5%
e 456287
6.5%
w 150261
 
2.2%
a 150261
 
2.2%
Other values (3) 450783
6.5%

CHANNEL_TYPE
Categorical

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.8 MiB
Credit and cash offices
719968 
Country-wide
494690 
Stone
212083 
Regional / Local
108528 
Contact center
 
71297
Other values (3)
 
63648

Length

Max length26
Median length23
Mean length16.351602
Min length5

Characters and Unicode

Total characters27310675
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCountry-wide
2nd rowContact center
3rd rowCredit and cash offices
4th rowCredit and cash offices
5th rowCredit and cash offices

Common Values

ValueCountFrequency (%)
Credit and cash offices 719968
43.1%
Country-wide 494690
29.6%
Stone 212083
 
12.7%
Regional / Local 108528
 
6.5%
Contact center 71297
 
4.3%
AP+ (Cash loan) 57046
 
3.4%
Channel of corporate sales 6150
 
0.4%
Car dealer 452
 
< 0.1%

Length

2025-02-02T14:52:31.593531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-02T14:52:31.815339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
cash 777014
18.3%
credit 719968
16.9%
offices 719968
16.9%
and 719968
16.9%
country-wide 494690
11.6%
stone 212083
 
5.0%
regional 108528
 
2.6%
108528
 
2.6%
local 108528
 
2.6%
center 71297
 
1.7%
Other values (9) 210893
 
5.0%

Most occurring characters

ValueCountFrequency (%)
2581251
 
9.5%
e 2417185
 
8.9%
i 2043154
 
7.5%
d 1935078
 
7.1%
a 1861735
 
6.8%
o 1790590
 
6.6%
n 1747209
 
6.4%
c 1697208
 
6.2%
t 1646782
 
6.0%
s 1509282
 
5.5%
Other values (20) 8081201
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27310675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2581251
 
9.5%
e 2417185
 
8.9%
i 2043154
 
7.5%
d 1935078
 
7.1%
a 1861735
 
6.8%
o 1790590
 
6.6%
n 1747209
 
6.4%
c 1697208
 
6.2%
t 1646782
 
6.0%
s 1509282
 
5.5%
Other values (20) 8081201
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27310675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2581251
 
9.5%
e 2417185
 
8.9%
i 2043154
 
7.5%
d 1935078
 
7.1%
a 1861735
 
6.8%
o 1790590
 
6.6%
n 1747209
 
6.4%
c 1697208
 
6.2%
t 1646782
 
6.0%
s 1509282
 
5.5%
Other values (20) 8081201
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27310675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2581251
 
9.5%
e 2417185
 
8.9%
i 2043154
 
7.5%
d 1935078
 
7.1%
a 1861735
 
6.8%
o 1790590
 
6.6%
n 1747209
 
6.4%
c 1697208
 
6.2%
t 1646782
 
6.0%
s 1509282
 
5.5%
Other values (20) 8081201
29.6%

SELLERPLACE_AREA
Real number (ℝ)

Skewed  Zeros 

Distinct2097
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean313.95112
Minimum-1
Maximum4000000
Zeros60523
Zeros (%)3.6%
Negative762675
Negative (%)45.7%
Memory size12.7 MiB
2025-02-02T14:52:32.131703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median3
Q382
95-th percentile1820
Maximum4000000
Range4000001
Interquartile range (IQR)83

Descriptive statistics

Standard deviation7127.4435
Coefficient of variation (CV)22.702399
Kurtosis296880.64
Mean313.95112
Median Absolute Deviation (MAD)4
Skewness529.62028
Sum5.2436555 × 108
Variance50800450
MonotonicityNot monotonic
2025-02-02T14:52:32.464177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 762675
45.7%
0 60523
 
3.6%
50 37401
 
2.2%
30 34423
 
2.1%
20 33840
 
2.0%
100 31409
 
1.9%
40 24429
 
1.5%
25 18142
 
1.1%
15 17175
 
1.0%
150 16652
 
1.0%
Other values (2087) 633545
37.9%
ValueCountFrequency (%)
-1 762675
45.7%
0 60523
 
3.6%
1 5275
 
0.3%
2 4374
 
0.3%
3 5472
 
0.3%
4 12797
 
0.8%
5 14942
 
0.9%
6 7411
 
0.4%
7 1413
 
0.1%
8 2022
 
0.1%
ValueCountFrequency (%)
4000000 5
 
< 0.1%
256099 1
 
< 0.1%
250000 9
 
< 0.1%
120000 3
 
< 0.1%
112000 4
 
< 0.1%
74625 376
< 0.1%
65489 6
 
< 0.1%
65339 5
 
< 0.1%
49151 36
 
< 0.1%
45000 1
 
< 0.1%

NAME_SELLER_INDUSTRY
Categorical

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size105.3 MiB
XNA
855720 
Consumer electronics
398265 
Connectivity
276029 
Furniture
 
57849
Construction
 
29781
Other values (6)
 
52570

Length

Max length20
Median length3
Mean length9.0886252
Min length3

Characters and Unicode

Total characters15179949
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConnectivity
2nd rowXNA
3rd rowXNA
4th rowXNA
5th rowXNA

Common Values

ValueCountFrequency (%)
XNA 855720
51.2%
Consumer electronics 398265
23.8%
Connectivity 276029
 
16.5%
Furniture 57849
 
3.5%
Construction 29781
 
1.8%
Clothing 23949
 
1.4%
Industry 19194
 
1.1%
Auto technology 4990
 
0.3%
Jewelry 2709
 
0.2%
MLM partners 1215
 
0.1%

Length

2025-02-02T14:52:32.844750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
xna 855720
41.2%
consumer 398265
19.2%
electronics 398265
19.2%
connectivity 276029
 
13.3%
furniture 57849
 
2.8%
construction 29781
 
1.4%
clothing 23949
 
1.2%
industry 19194
 
0.9%
auto 4990
 
0.2%
technology 4990
 
0.2%
Other values (4) 5652
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 1540296
 
10.1%
n 1515347
 
10.0%
o 1171553
 
7.7%
t 1122072
 
7.4%
c 1107330
 
7.3%
i 1062415
 
7.0%
r 966855
 
6.4%
A 860710
 
5.7%
N 855720
 
5.6%
X 855720
 
5.6%
Other values (20) 4121931
27.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15179949
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1540296
 
10.1%
n 1515347
 
10.0%
o 1171553
 
7.7%
t 1122072
 
7.4%
c 1107330
 
7.3%
i 1062415
 
7.0%
r 966855
 
6.4%
A 860710
 
5.7%
N 855720
 
5.6%
X 855720
 
5.6%
Other values (20) 4121931
27.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15179949
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1540296
 
10.1%
n 1515347
 
10.0%
o 1171553
 
7.7%
t 1122072
 
7.4%
c 1107330
 
7.3%
i 1062415
 
7.0%
r 966855
 
6.4%
A 860710
 
5.7%
N 855720
 
5.6%
X 855720
 
5.6%
Other values (20) 4121931
27.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15179949
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1540296
 
10.1%
n 1515347
 
10.0%
o 1171553
 
7.7%
t 1122072
 
7.4%
c 1107330
 
7.3%
i 1062415
 
7.0%
r 966855
 
6.4%
A 860710
 
5.7%
N 855720
 
5.6%
X 855720
 
5.6%
Other values (20) 4121931
27.2%

CNT_PAYMENT
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct49
Distinct (%)< 0.1%
Missing372230
Missing (%)22.3%
Infinite0
Infinite (%)0.0%
Mean16.054082
Minimum0
Maximum84
Zeros144985
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size12.7 MiB
2025-02-02T14:52:33.114399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median12
Q324
95-th percentile48
Maximum84
Range84
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.567288
Coefficient of variation (CV)0.90738842
Kurtosis1.868014
Mean16.054082
Median Absolute Deviation (MAD)6
Skewness1.531403
Sum20837941
Variance212.20587
MonotonicityNot monotonic
2025-02-02T14:52:33.400182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
12 323049
19.3%
6 190461
11.4%
0 144985
 
8.7%
10 141851
 
8.5%
24 137764
 
8.2%
18 77430
 
4.6%
36 72583
 
4.3%
60 53600
 
3.2%
48 47316
 
2.8%
8 30349
 
1.8%
Other values (39) 78596
 
4.7%
(Missing) 372230
22.3%
ValueCountFrequency (%)
0 144985
8.7%
3 1100
 
0.1%
4 26924
 
1.6%
5 3957
 
0.2%
6 190461
11.4%
7 1434
 
0.1%
8 30349
 
1.8%
9 1236
 
0.1%
10 141851
8.5%
11 669
 
< 0.1%
ValueCountFrequency (%)
84 45
 
< 0.1%
72 139
 
< 0.1%
66 10
 
< 0.1%
60 53600
3.2%
59 4
 
< 0.1%
54 2104
 
0.1%
53 1
 
< 0.1%
48 47316
2.8%
47 3
 
< 0.1%
46 2
 
< 0.1%

NAME_YIELD_GROUP
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.8 MiB
XNA
517215 
middle
385532 
high
353331 
low_normal
322095 
low_action
92041 

Length

Max length10
Median length6
Mean length5.639709
Min length3

Characters and Unicode

Total characters9419521
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmiddle
2nd rowlow_action
3rd rowhigh
4th rowmiddle
5th rowhigh

Common Values

ValueCountFrequency (%)
XNA 517215
31.0%
middle 385532
23.1%
high 353331
21.2%
low_normal 322095
19.3%
low_action 92041
 
5.5%

Length

2025-02-02T14:52:33.891569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-02T14:52:34.066154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
xna 517215
31.0%
middle 385532
23.1%
high 353331
21.2%
low_normal 322095
19.3%
low_action 92041
 
5.5%

Most occurring characters

ValueCountFrequency (%)
l 1121763
11.9%
i 830904
 
8.8%
o 828272
 
8.8%
d 771064
 
8.2%
m 707627
 
7.5%
h 706662
 
7.5%
X 517215
 
5.5%
A 517215
 
5.5%
N 517215
 
5.5%
a 414136
 
4.4%
Other values (8) 2487448
26.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9419521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 1121763
11.9%
i 830904
 
8.8%
o 828272
 
8.8%
d 771064
 
8.2%
m 707627
 
7.5%
h 706662
 
7.5%
X 517215
 
5.5%
A 517215
 
5.5%
N 517215
 
5.5%
a 414136
 
4.4%
Other values (8) 2487448
26.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9419521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 1121763
11.9%
i 830904
 
8.8%
o 828272
 
8.8%
d 771064
 
8.2%
m 707627
 
7.5%
h 706662
 
7.5%
X 517215
 
5.5%
A 517215
 
5.5%
N 517215
 
5.5%
a 414136
 
4.4%
Other values (8) 2487448
26.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9419521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 1121763
11.9%
i 830904
 
8.8%
o 828272
 
8.8%
d 771064
 
8.2%
m 707627
 
7.5%
h 706662
 
7.5%
X 517215
 
5.5%
A 517215
 
5.5%
N 517215
 
5.5%
a 414136
 
4.4%
Other values (8) 2487448
26.4%

PRODUCT_COMBINATION
Categorical

High correlation 

Distinct17
Distinct (%)< 0.1%
Missing346
Missing (%)< 0.1%
Memory size119.8 MiB
Cash
285990 
POS household with interest
263622 
POS mobile with interest
220670 
Cash X-Sell: middle
143883 
Cash X-Sell: low
130248 
Other values (12)
625455 

Length

Max length30
Median length26
Mean length18.212805
Min length4

Characters and Unicode

Total characters30412981
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPOS mobile with interest
2nd rowCash X-Sell: low
3rd rowCash X-Sell: high
4th rowCash X-Sell: middle
5th rowCash Street: high

Common Values

ValueCountFrequency (%)
Cash 285990
17.1%
POS household with interest 263622
15.8%
POS mobile with interest 220670
13.2%
Cash X-Sell: middle 143883
8.6%
Cash X-Sell: low 130248
7.8%
Card Street 112582
 
6.7%
POS industry with interest 98833
 
5.9%
POS household without interest 82908
 
5.0%
Card X-Sell 80582
 
4.8%
Cash Street: high 59639
 
3.6%
Other values (7) 190911
11.4%

Length

2025-02-02T14:52:34.287041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cash 747553
15.0%
pos 729151
14.7%
interest 729151
14.7%
with 607004
12.2%
x-sell 414014
8.3%
household 346530
7.0%
mobile 244752
 
4.9%
street 240713
 
4.8%
card 193164
 
3.9%
middle 178541
 
3.6%
Other values (6) 543038
10.9%

Most occurring characters

ValueCountFrequency (%)
3303743
 
10.9%
e 3149999
 
10.4%
t 2928895
 
9.6%
h 2434078
 
8.0%
i 2111970
 
6.9%
s 1937224
 
6.4%
l 1761933
 
5.8%
S 1383878
 
4.6%
r 1300897
 
4.3%
o 1250475
 
4.1%
Other values (15) 8849889
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30412981
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3303743
 
10.9%
e 3149999
 
10.4%
t 2928895
 
9.6%
h 2434078
 
8.0%
i 2111970
 
6.9%
s 1937224
 
6.4%
l 1761933
 
5.8%
S 1383878
 
4.6%
r 1300897
 
4.3%
o 1250475
 
4.1%
Other values (15) 8849889
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30412981
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3303743
 
10.9%
e 3149999
 
10.4%
t 2928895
 
9.6%
h 2434078
 
8.0%
i 2111970
 
6.9%
s 1937224
 
6.4%
l 1761933
 
5.8%
S 1383878
 
4.6%
r 1300897
 
4.3%
o 1250475
 
4.1%
Other values (15) 8849889
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30412981
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3303743
 
10.9%
e 3149999
 
10.4%
t 2928895
 
9.6%
h 2434078
 
8.0%
i 2111970
 
6.9%
s 1937224
 
6.4%
l 1761933
 
5.8%
S 1383878
 
4.6%
r 1300897
 
4.3%
o 1250475
 
4.1%
Other values (15) 8849889
29.1%

DAYS_FIRST_DRAWING
Real number (ℝ)

High correlation  Missing 

Distinct2838
Distinct (%)0.3%
Missing673065
Missing (%)40.3%
Infinite0
Infinite (%)0.0%
Mean342209.86
Minimum-2922
Maximum365243
Zeros0
Zeros (%)0.0%
Negative62705
Negative (%)3.8%
Memory size12.7 MiB
2025-02-02T14:52:34.399658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2922
5-th percentile-269
Q1365243
median365243
Q3365243
95-th percentile365243
Maximum365243
Range368165
Interquartile range (IQR)0

Descriptive statistics

Standard deviation88916.116
Coefficient of variation (CV)0.25982921
Kurtosis10.969807
Mean342209.86
Median Absolute Deviation (MAD)0
Skewness-3.6013428
Sum3.4123421 × 1011
Variance7.9060757 × 109
MonotonicityNot monotonic
2025-02-02T14:52:34.558958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365243 934444
55.9%
-228 123
 
< 0.1%
-212 121
 
< 0.1%
-224 121
 
< 0.1%
-223 119
 
< 0.1%
-220 118
 
< 0.1%
-210 117
 
< 0.1%
-235 117
 
< 0.1%
-240 116
 
< 0.1%
-226 115
 
< 0.1%
Other values (2828) 61638
 
3.7%
(Missing) 673065
40.3%
ValueCountFrequency (%)
-2922 1
 
< 0.1%
-2921 2
 
< 0.1%
-2920 5
< 0.1%
-2919 12
< 0.1%
-2918 7
< 0.1%
-2917 5
< 0.1%
-2916 6
< 0.1%
-2915 7
< 0.1%
-2914 4
 
< 0.1%
-2913 9
< 0.1%
ValueCountFrequency (%)
365243 934444
55.9%
-2 20
 
< 0.1%
-3 14
 
< 0.1%
-4 10
 
< 0.1%
-5 14
 
< 0.1%
-6 20
 
< 0.1%
-7 20
 
< 0.1%
-8 15
 
< 0.1%
-9 21
 
< 0.1%
-10 22
 
< 0.1%

DAYS_FIRST_DUE
Real number (ℝ)

High correlation  Missing 

Distinct2892
Distinct (%)0.3%
Missing673065
Missing (%)40.3%
Infinite0
Infinite (%)0.0%
Mean13826.269
Minimum-2892
Maximum365243
Zeros0
Zeros (%)0.0%
Negative956504
Negative (%)57.3%
Memory size12.7 MiB
2025-02-02T14:52:34.725121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2892
5-th percentile-2608
Q1-1628
median-831
Q3-411
95-th percentile-48
Maximum365243
Range368135
Interquartile range (IQR)1217

Descriptive statistics

Standard deviation72444.87
Coefficient of variation (CV)5.2396542
Kurtosis19.570596
Mean13826.269
Median Absolute Deviation (MAD)525
Skewness4.6440959
Sum1.3786851 × 1010
Variance5.2482591 × 109
MonotonicityNot monotonic
2025-02-02T14:52:34.906992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365243 40645
 
2.4%
-334 772
 
< 0.1%
-509 760
 
< 0.1%
-208 751
 
< 0.1%
-330 750
 
< 0.1%
-292 746
 
< 0.1%
-691 745
 
< 0.1%
-270 744
 
< 0.1%
-299 744
 
< 0.1%
-327 743
 
< 0.1%
Other values (2882) 949749
56.9%
(Missing) 673065
40.3%
ValueCountFrequency (%)
-2892 9
 
< 0.1%
-2891 55
< 0.1%
-2890 73
< 0.1%
-2889 86
< 0.1%
-2888 96
< 0.1%
-2887 87
< 0.1%
-2886 92
< 0.1%
-2885 121
< 0.1%
-2884 113
< 0.1%
-2883 136
< 0.1%
ValueCountFrequency (%)
365243 40645
2.4%
-2 14
 
< 0.1%
-3 136
 
< 0.1%
-4 132
 
< 0.1%
-5 182
 
< 0.1%
-6 175
 
< 0.1%
-7 157
 
< 0.1%
-8 156
 
< 0.1%
-9 176
 
< 0.1%
-10 168
 
< 0.1%

DAYS_LAST_DUE_1ST_VERSION
Real number (ℝ)

High correlation  Missing 

Distinct4605
Distinct (%)0.5%
Missing673065
Missing (%)40.3%
Infinite0
Infinite (%)0.0%
Mean33767.774
Minimum-2801
Maximum365243
Zeros705
Zeros (%)< 0.1%
Negative678188
Negative (%)40.6%
Memory size12.7 MiB
2025-02-02T14:52:35.056638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2801
5-th percentile-2327
Q1-1242
median-361
Q3129
95-th percentile365243
Maximum365243
Range368044
Interquartile range (IQR)1371

Descriptive statistics

Standard deviation106857.03
Coefficient of variation (CV)3.1644678
Kurtosis5.7261481
Mean33767.774
Median Absolute Deviation (MAD)639
Skewness2.7794499
Sum3.3671502 × 1010
Variance1.1418426 × 1010
MonotonicityNot monotonic
2025-02-02T14:52:35.193140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365243 93864
 
5.6%
9 720
 
< 0.1%
8 706
 
< 0.1%
0 705
 
< 0.1%
5 702
 
< 0.1%
10 698
 
< 0.1%
2 688
 
< 0.1%
6 685
 
< 0.1%
1 685
 
< 0.1%
-1 675
 
< 0.1%
Other values (4595) 897021
53.7%
(Missing) 673065
40.3%
ValueCountFrequency (%)
-2801 9
 
< 0.1%
-2800 9
 
< 0.1%
-2799 6
 
< 0.1%
-2798 18
< 0.1%
-2797 11
< 0.1%
-2796 24
< 0.1%
-2795 14
< 0.1%
-2794 17
< 0.1%
-2793 22
< 0.1%
-2792 11
< 0.1%
ValueCountFrequency (%)
365243 93864
5.6%
2389 1
 
< 0.1%
2098 1
 
< 0.1%
2090 1
 
< 0.1%
2032 1
 
< 0.1%
2016 1
 
< 0.1%
2011 1
 
< 0.1%
1993 1
 
< 0.1%
1990 1
 
< 0.1%
1954 1
 
< 0.1%

DAYS_LAST_DUE
Real number (ℝ)

High correlation  Missing 

Distinct2873
Distinct (%)0.3%
Missing673065
Missing (%)40.3%
Infinite0
Infinite (%)0.0%
Mean76582.403
Minimum-2889
Maximum365243
Zeros0
Zeros (%)0.0%
Negative785928
Negative (%)47.1%
Memory size12.7 MiB
2025-02-02T14:52:35.319408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2889
5-th percentile-2349
Q1-1314
median-537
Q3-74
95-th percentile365243
Maximum365243
Range368132
Interquartile range (IQR)1240

Descriptive statistics

Standard deviation149647.42
Coefficient of variation (CV)1.9540705
Kurtosis-0.01044733
Mean76582.403
Median Absolute Deviation (MAD)618
Skewness1.4104726
Sum7.6364067 × 1010
Variance2.2394349 × 1010
MonotonicityNot monotonic
2025-02-02T14:52:35.572490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365243 211221
 
12.6%
-245 658
 
< 0.1%
-188 650
 
< 0.1%
-239 642
 
< 0.1%
-167 638
 
< 0.1%
-247 629
 
< 0.1%
-305 627
 
< 0.1%
-268 624
 
< 0.1%
-236 623
 
< 0.1%
-160 623
 
< 0.1%
Other values (2863) 780214
46.7%
(Missing) 673065
40.3%
ValueCountFrequency (%)
-2889 1
 
< 0.1%
-2888 1
 
< 0.1%
-2885 1
 
< 0.1%
-2884 2
< 0.1%
-2883 3
< 0.1%
-2881 1
 
< 0.1%
-2878 3
< 0.1%
-2876 1
 
< 0.1%
-2869 2
< 0.1%
-2867 1
 
< 0.1%
ValueCountFrequency (%)
365243 211221
12.6%
-2 30
 
< 0.1%
-3 402
 
< 0.1%
-4 501
 
< 0.1%
-5 444
 
< 0.1%
-6 531
 
< 0.1%
-7 559
 
< 0.1%
-8 548
 
< 0.1%
-9 568
 
< 0.1%
-10 569
 
< 0.1%

DAYS_TERMINATION
Real number (ℝ)

High correlation  Missing 

Distinct2830
Distinct (%)0.3%
Missing673065
Missing (%)40.3%
Infinite0
Infinite (%)0.0%
Mean81992.344
Minimum-2874
Maximum365243
Zeros0
Zeros (%)0.0%
Negative771236
Negative (%)46.2%
Memory size12.7 MiB
2025-02-02T14:52:35.920162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2874
5-th percentile-2331
Q1-1270
median-499
Q3-44
95-th percentile365243
Maximum365243
Range368117
Interquartile range (IQR)1226

Descriptive statistics

Standard deviation153303.52
Coefficient of variation (CV)1.8697297
Kurtosis-0.29327669
Mean81992.344
Median Absolute Deviation (MAD)672
Skewness1.306376
Sum8.1758584 × 1010
Variance2.3501968 × 1010
MonotonicityNot monotonic
2025-02-02T14:52:36.261605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365243 225913
 
13.5%
-233 786
 
< 0.1%
-170 770
 
< 0.1%
-184 770
 
< 0.1%
-163 769
 
< 0.1%
-169 760
 
< 0.1%
-303 754
 
< 0.1%
-177 753
 
< 0.1%
-305 742
 
< 0.1%
-212 741
 
< 0.1%
Other values (2820) 764391
45.8%
(Missing) 673065
40.3%
ValueCountFrequency (%)
-2874 1
 
< 0.1%
-2870 1
 
< 0.1%
-2865 1
 
< 0.1%
-2852 1
 
< 0.1%
-2848 1
 
< 0.1%
-2847 1
 
< 0.1%
-2845 2
< 0.1%
-2844 1
 
< 0.1%
-2839 3
< 0.1%
-2837 1
 
< 0.1%
ValueCountFrequency (%)
365243 225913
13.5%
-2 602
 
< 0.1%
-3 597
 
< 0.1%
-4 636
 
< 0.1%
-5 638
 
< 0.1%
-6 447
 
< 0.1%
-7 223
 
< 0.1%
-8 688
 
< 0.1%
-9 711
 
< 0.1%
-10 605
 
< 0.1%

NFLAG_INSURED_ON_APPROVAL
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing673065
Missing (%)40.3%
Memory size93.0 MiB
0.0
665527 
1.0
331622 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2991447
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 665527
39.8%
1.0 331622
19.9%
(Missing) 673065
40.3%

Length

2025-02-02T14:52:36.550047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-02T14:52:36.692190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 665527
66.7%
1.0 331622
33.3%

Most occurring characters

ValueCountFrequency (%)
0 1662676
55.6%
. 997149
33.3%
1 331622
 
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2991447
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1662676
55.6%
. 997149
33.3%
1 331622
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2991447
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1662676
55.6%
. 997149
33.3%
1 331622
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2991447
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1662676
55.6%
. 997149
33.3%
1 331622
 
11.1%

Interactions

2025-02-02T14:51:34.825419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:05.792766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:17.868699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:27.554495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:36.379848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:45.468188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:54.762470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:01.337470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:10.109289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:19.131504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:25.773774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:29.511753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:33.691036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:43.548896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:52.993748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:01.788635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:10.502000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:18.645420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:27.070656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:35.380410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:06.683909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:18.607537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:28.219856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:37.107994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:46.230756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:55.248003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:01.728391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:10.457225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:19.370470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:26.026950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:29.813059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:34.442556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:44.331785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:53.699174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:02.376783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:11.114886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:18.936620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:27.373836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:35.922139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:07.604048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:19.265211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:28.743557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:37.740353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:46.880326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:55.711184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:02.385907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:10.937898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:19.607661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:26.294020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:30.082726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:35.119304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:45.025680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:54.370428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:02.913360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:11.684386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:19.347109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:27.660657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:36.302896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:08.254233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:19.778589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:29.076950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:38.057728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:47.261982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:56.044564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:03.021933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:11.637784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:19.892613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:26.556881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:30.336776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:35.866308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:45.850029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:55.173337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-02T14:50:07.811895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:16.786556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:24.069500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:28.498082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:32.727369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:41.530198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:50.857060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:59.948623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:08.021868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:16.603169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:24.394453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:32.314825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:40.981366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:15.406501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:25.150687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:34.022702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:43.198442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:52.541154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:00.188867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:08.333512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:17.331408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:24.301141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:28.640350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:32.843891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:41.810273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:51.135927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:00.218991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:08.568778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:17.137195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:24.930071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:32.695477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:41.269198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:15.976648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:25.721844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:34.455835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:43.547378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:53.068215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:00.569185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:08.868807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:17.930892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:24.641495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:28.753357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:32.967809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:42.114642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:51.411860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:00.496972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:09.051813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:17.728467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:25.469881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:33.219618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:41.568480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:16.559204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:26.317718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:34.999667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:44.122667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:53.590630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:00.805641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:09.384689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:18.533103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:25.065508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:28.878035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:33.100559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:42.395964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:51.698745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:00.769855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:09.588999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:18.093069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:26.009399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:33.732710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:41.855839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:17.119666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:26.873243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:35.578491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:44.660278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:49:54.287465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:01.004580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:09.764792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:18.893627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:25.519998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:29.086828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:33.218170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:42.768187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:50:52.274297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:01.258688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:09.921406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:18.362143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:26.547786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-02T14:51:34.285492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-02T14:52:37.028492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AMT_ANNUITYAMT_APPLICATIONAMT_CREDITAMT_DOWN_PAYMENTAMT_GOODS_PRICECHANNEL_TYPECNT_PAYMENTCODE_REJECT_REASONDAYS_DECISIONDAYS_FIRST_DRAWINGDAYS_FIRST_DUEDAYS_LAST_DUEDAYS_LAST_DUE_1ST_VERSIONDAYS_TERMINATIONFLAG_LAST_APPL_PER_CONTRACTHOUR_APPR_PROCESS_STARTNAME_CASH_LOAN_PURPOSENAME_CLIENT_TYPENAME_CONTRACT_STATUSNAME_CONTRACT_TYPENAME_GOODS_CATEGORYNAME_PAYMENT_TYPENAME_PORTFOLIONAME_PRODUCT_TYPENAME_SELLER_INDUSTRYNAME_TYPE_SUITENAME_YIELD_GROUPNFLAG_INSURED_ON_APPROVALNFLAG_LAST_APPL_IN_DAYPRODUCT_COMBINATIONRATE_DOWN_PAYMENTRATE_INTEREST_PRIMARYRATE_INTEREST_PRIVILEGEDSELLERPLACE_AREASK_ID_CURRSK_ID_PREVWEEKDAY_APPR_PROCESS_START
AMT_ANNUITY1.0000.8300.8800.0440.8890.1020.3920.0460.2820.0700.2320.2730.2720.2500.014-0.0490.1000.0620.0620.2020.0800.0240.1650.1710.0890.0370.0740.1630.0110.116-0.178-0.092-0.186-0.4130.0010.0130.020
AMT_APPLICATION0.8301.0000.9170.0261.0000.1180.6380.091-0.1220.1930.2760.3310.3050.2780.0060.0140.1010.0590.1100.1360.0700.0550.2060.2320.0730.0470.1490.1390.0030.183-0.198-0.128-0.2380.0480.001-0.0120.018
AMT_CREDIT0.8800.9171.000-0.0950.9850.1040.5490.104-0.155-0.1050.3090.4350.5080.4380.0180.0030.1250.0670.1210.1530.0890.0600.2050.2650.0950.0530.1590.1730.0160.225-0.327-0.086-0.184-0.0090.000-0.0120.020
AMT_DOWN_PAYMENT0.0440.026-0.0951.0000.0260.189-0.2220.028-0.234-0.001-0.225-0.265-0.265-0.2620.0000.0340.0000.0030.0290.0020.0190.0000.2310.0020.0270.0000.0100.0060.0000.0140.918-0.277-0.4090.0550.003-0.0040.001
AMT_GOODS_PRICE0.8891.0000.9850.0261.0000.1550.6000.0950.355-0.0390.2970.4080.4770.4050.016-0.0590.1260.0840.1210.2360.1040.0100.1970.2340.1120.0470.1360.1340.0100.180-0.198-0.128-0.238-0.4560.0010.0150.025
CHANNEL_TYPE0.1020.1180.1040.1890.1551.0000.2240.1170.1870.2700.1130.2670.2990.2800.0610.0780.3000.2430.3310.5240.3930.2520.6560.4090.4370.1490.2600.4980.0460.4240.0900.4320.5920.0020.0030.0190.076
CNT_PAYMENT0.3920.6380.549-0.2220.6000.2241.0000.1260.1750.4120.0510.1490.1260.0970.122-0.0480.2240.1340.1930.5700.1790.2090.4900.3650.1780.0830.3270.3340.0870.320-0.3350.072-0.055-0.255-0.0000.0100.041
CODE_REJECT_REASON0.0460.0910.1040.0280.0950.1170.1261.0000.1000.0000.0000.0000.0000.0000.3010.0200.1180.1080.8160.1670.1140.0920.2080.2780.1110.0560.1240.0000.2350.1860.0670.1090.0000.0000.0020.0120.021
DAYS_DECISION0.282-0.122-0.155-0.2340.3550.1870.1750.1001.000-0.0270.9740.8970.7950.8490.045-0.0430.1310.1870.3170.2580.1670.2380.2910.1620.1660.1080.3040.2520.0390.239-0.2940.4550.638-0.404-0.0000.0230.033
DAYS_FIRST_DRAWING0.0700.193-0.105-0.001-0.0390.2700.4120.000-0.0271.000-0.042-0.184-0.406-0.3191.0000.0140.1650.1281.0000.8040.3250.5140.8040.3340.2240.0810.8040.1780.0000.804-0.001NaNNaN0.162-0.001-0.0010.042
DAYS_FIRST_DUE0.2320.2760.309-0.2250.2970.1130.0510.0000.974-0.0421.0000.9180.8280.8681.000-0.0000.1270.1001.0000.5120.1900.3300.5120.2410.1080.0460.5120.1190.0020.513-0.2880.4620.651-0.173-0.0000.0020.019
DAYS_LAST_DUE0.2730.3310.435-0.2650.4080.2670.1490.0000.897-0.1840.9181.0000.9030.9631.000-0.0130.0530.1881.0000.4410.3080.2610.4410.3010.2770.1380.4640.0130.0020.492-0.3420.4440.605-0.2330.0010.0030.045
DAYS_LAST_DUE_1ST_VERSION0.2720.3050.508-0.2650.4770.2990.1260.0000.795-0.4060.8280.9031.0000.9421.000-0.0290.2060.1501.0000.9990.4050.6390.9990.4320.2680.0900.9990.2230.0010.999-0.3430.4610.647-0.3180.0000.0030.049
DAYS_TERMINATION0.2500.2780.438-0.2620.4050.2800.0970.0000.849-0.3190.8680.9630.9421.0001.000-0.0150.0380.1971.0000.5070.3300.3060.5070.3220.2770.1360.5270.0030.0000.550-0.3390.4420.602-0.2370.0010.0030.047
FLAG_LAST_APPL_PER_CONTRACT0.0140.0060.0180.0000.0160.0610.1220.3010.0451.0001.0001.0001.0001.0001.0000.0080.0640.0340.1560.1970.0620.0920.2310.1160.0290.0200.1071.0000.7220.2490.0001.0001.0000.0000.0020.0070.009
HOUR_APPR_PROCESS_START-0.0490.0140.0030.034-0.0590.078-0.0480.020-0.0430.014-0.000-0.013-0.029-0.0150.0081.0000.0470.0430.0490.0900.0580.0610.0720.0720.0500.0360.0410.1190.0060.0600.029-0.051-0.0590.1390.003-0.0030.025
NAME_CASH_LOAN_PURPOSE0.1000.1010.1250.0000.1260.3000.2240.1180.1310.1650.1270.0530.2060.0380.0640.0471.0000.2130.2960.5770.1600.1330.4720.5690.2410.1310.1410.6360.0450.3120.0751.0001.0000.0000.0040.0090.066
NAME_CLIENT_TYPE0.0620.0590.0670.0030.0840.2430.1340.1080.1870.1280.1000.1880.1500.1970.0340.0430.2131.0000.1870.2550.2570.1210.2640.2050.2410.1050.1660.1760.0240.2650.0860.0260.1520.0020.0040.0110.060
NAME_CONTRACT_STATUS0.0620.1100.1210.0290.1210.3310.1930.8160.3171.0001.0001.0001.0001.0000.1560.0490.2960.1871.0000.2970.3150.3610.5500.3110.3270.0750.4311.0000.1100.5560.1001.0001.0000.0000.0010.0310.058
NAME_CONTRACT_TYPE0.2020.1360.1530.0020.2360.5240.5700.1670.2580.8040.5120.4410.9990.5070.1970.0900.5770.2550.2971.0000.5700.3290.7410.4810.5220.2520.3740.6420.1431.0000.1611.0001.0000.0010.0030.0170.110
NAME_GOODS_CATEGORY0.0800.0700.0890.0190.1040.3930.1790.1140.1670.3250.1900.3080.4050.3300.0620.0580.1600.2570.3150.5701.0000.2640.4750.4640.7280.1530.3320.4660.0460.4370.0930.2730.3150.0290.0030.0100.081
NAME_PAYMENT_TYPE0.0240.0550.0600.0000.0100.2520.2090.0920.2380.5140.3300.2610.6390.3060.0920.0610.1330.1210.3610.3290.2641.0000.4430.0550.2320.0420.4420.1950.0640.4720.0250.2440.3810.0000.0030.0170.048
NAME_PORTFOLIO0.1650.2060.2050.2310.1970.6560.4900.2080.2910.8040.5120.4410.9990.5070.2310.0720.4720.2640.5500.7410.4750.4431.0000.7130.4410.2050.5050.6420.1690.8060.1531.0001.0000.0010.0030.0210.093
NAME_PRODUCT_TYPE0.1710.2320.2650.0020.2340.4090.3650.2780.1620.3340.2410.3010.4320.3220.1160.0720.5690.2050.3110.4810.4640.0550.7131.0000.3750.2510.1580.4730.0890.8880.1591.0001.0000.0010.0030.0060.090
NAME_SELLER_INDUSTRY0.0890.0730.0950.0270.1120.4370.1780.1110.1660.2240.1080.2770.2680.2770.0290.0500.2410.2410.3270.5220.7280.2320.4410.3751.0000.1550.3120.4780.0190.5600.0870.2640.3220.0080.0020.0100.077
NAME_TYPE_SUITE0.0370.0470.0530.0000.0470.1490.0830.0560.1080.0810.0460.1380.0900.1360.0200.0360.1310.1050.0750.2520.1530.0420.2050.2510.1551.0000.0600.1320.0160.1560.0330.0000.0000.0000.0020.0050.056
NAME_YIELD_GROUP0.0740.1490.1590.0100.1360.2600.3270.1240.3040.8040.5120.4640.9990.5270.1070.0410.1410.1660.4310.3740.3320.4420.5050.1580.3120.0601.0000.2560.0740.7650.1681.0001.0000.0050.0020.0160.044
NFLAG_INSURED_ON_APPROVAL0.1630.1390.1730.0060.1340.4980.3340.0000.2520.1780.1190.0130.2230.0031.0000.1190.6360.1761.0000.6420.4660.1950.6420.4730.4780.1320.2561.0000.0070.6590.0540.3370.1860.0000.0040.0060.086
NFLAG_LAST_APPL_IN_DAY0.0110.0030.0160.0000.0100.0460.0870.2350.0390.0000.0020.0020.0010.0000.7220.0060.0450.0240.1100.1430.0460.0640.1690.0890.0190.0160.0740.0071.0000.1780.0060.0000.0150.0000.0020.0040.022
PRODUCT_COMBINATION0.1160.1830.2250.0140.1800.4240.3200.1860.2390.8040.5130.4920.9990.5500.2490.0600.3120.2650.5561.0000.4370.4720.8060.8880.5600.1560.7650.6590.1781.0000.1210.3800.5110.0160.0030.0130.081
RATE_DOWN_PAYMENT-0.178-0.198-0.3270.918-0.1980.090-0.3350.067-0.294-0.001-0.288-0.342-0.343-0.3390.0000.0290.0750.0860.1000.1610.0930.0250.1530.1590.0870.0330.1680.0540.0060.1211.000-0.237-0.318-0.0230.002-0.0050.010
RATE_INTEREST_PRIMARY-0.092-0.128-0.086-0.277-0.1280.4320.0720.1090.455NaN0.4620.4440.4610.4421.000-0.0511.0000.0261.0001.0000.2730.2441.0001.0000.2640.0001.0000.3370.0000.380-0.2371.0000.782-0.047-0.002-0.0090.041
RATE_INTEREST_PRIVILEGED-0.186-0.238-0.184-0.409-0.2380.592-0.0550.0000.638NaN0.6510.6050.6470.6021.000-0.0591.0000.1521.0001.0000.3150.3811.0001.0000.3220.0001.0000.1860.0150.511-0.3180.7821.000-0.094-0.029-0.0190.027
SELLERPLACE_AREA-0.4130.048-0.0090.055-0.4560.002-0.2550.000-0.4040.162-0.173-0.233-0.318-0.2370.0000.1390.0000.0020.0000.0010.0290.0000.0010.0010.0080.0000.0050.0000.0000.016-0.023-0.047-0.0941.000-0.001-0.0270.001
SK_ID_CURR0.0010.0010.0000.0030.0010.003-0.0000.002-0.000-0.001-0.0000.0010.0000.0010.0020.0030.0040.0040.0010.0030.0030.0030.0030.0030.0020.0020.0020.0040.0020.0030.002-0.002-0.029-0.0011.000-0.0000.002
SK_ID_PREV0.013-0.012-0.012-0.0040.0150.0190.0100.0120.023-0.0010.0020.0030.0030.0030.007-0.0030.0090.0110.0310.0170.0100.0170.0210.0060.0100.0050.0160.0060.0040.013-0.005-0.009-0.019-0.027-0.0001.0000.002
WEEKDAY_APPR_PROCESS_START0.0200.0180.0200.0010.0250.0760.0410.0210.0330.0420.0190.0450.0490.0470.0090.0250.0660.0600.0580.1100.0810.0480.0930.0900.0770.0560.0440.0860.0220.0810.0100.0410.0270.0010.0020.0021.000

Missing values

2025-02-02T14:51:44.798216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-02T14:51:53.949396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-02T14:52:15.303129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

SK_ID_PREVSK_ID_CURRNAME_CONTRACT_TYPEAMT_ANNUITYAMT_APPLICATIONAMT_CREDITAMT_DOWN_PAYMENTAMT_GOODS_PRICEWEEKDAY_APPR_PROCESS_STARTHOUR_APPR_PROCESS_STARTFLAG_LAST_APPL_PER_CONTRACTNFLAG_LAST_APPL_IN_DAYRATE_DOWN_PAYMENTRATE_INTEREST_PRIMARYRATE_INTEREST_PRIVILEGEDNAME_CASH_LOAN_PURPOSENAME_CONTRACT_STATUSDAYS_DECISIONNAME_PAYMENT_TYPECODE_REJECT_REASONNAME_TYPE_SUITENAME_CLIENT_TYPENAME_GOODS_CATEGORYNAME_PORTFOLIONAME_PRODUCT_TYPECHANNEL_TYPESELLERPLACE_AREANAME_SELLER_INDUSTRYCNT_PAYMENTNAME_YIELD_GROUPPRODUCT_COMBINATIONDAYS_FIRST_DRAWINGDAYS_FIRST_DUEDAYS_LAST_DUE_1ST_VERSIONDAYS_LAST_DUEDAYS_TERMINATIONNFLAG_INSURED_ON_APPROVAL
02030495271877Consumer loans1730.43017145.017145.00.017145.0SATURDAY15Y10.00.1828320.867336XAPApproved-73Cash through the bankXAPNaNRepeaterMobilePOSXNACountry-wide35Connectivity12.0middlePOS mobile with interest365243.0-42.0300.0-42.0-37.00.0
12802425108129Cash loans25188.615607500.0679671.0NaN607500.0THURSDAY11Y1NaNNaNNaNXNAApproved-164XNAXAPUnaccompaniedRepeaterXNACashx-sellContact center-1XNA36.0low_actionCash X-Sell: low365243.0-134.0916.0365243.0365243.01.0
22523466122040Cash loans15060.735112500.0136444.5NaN112500.0TUESDAY11Y1NaNNaNNaNXNAApproved-301Cash through the bankXAPSpouse, partnerRepeaterXNACashx-sellCredit and cash offices-1XNA12.0highCash X-Sell: high365243.0-271.059.0365243.0365243.01.0
32819243176158Cash loans47041.335450000.0470790.0NaN450000.0MONDAY7Y1NaNNaNNaNXNAApproved-512Cash through the bankXAPNaNRepeaterXNACashx-sellCredit and cash offices-1XNA12.0middleCash X-Sell: middle365243.0-482.0-152.0-182.0-177.01.0
41784265202054Cash loans31924.395337500.0404055.0NaN337500.0THURSDAY9Y1NaNNaNNaNRepairsRefused-781Cash through the bankHCNaNRepeaterXNACashwalk-inCredit and cash offices-1XNA24.0highCash Street: highNaNNaNNaNNaNNaNNaN
51383531199383Cash loans23703.930315000.0340573.5NaN315000.0SATURDAY8Y1NaNNaNNaNEveryday expensesApproved-684Cash through the bankXAPFamilyRepeaterXNACashx-sellCredit and cash offices-1XNA18.0low_normalCash X-Sell: low365243.0-654.0-144.0-144.0-137.01.0
62315218175704Cash loansNaN0.00.0NaNNaNTUESDAY11Y1NaNNaNNaNXNACanceled-14XNAXAPNaNRepeaterXNAXNAXNACredit and cash offices-1XNANaNXNACashNaNNaNNaNNaNNaNNaN
71656711296299Cash loansNaN0.00.0NaNNaNMONDAY7Y1NaNNaNNaNXNACanceled-21XNAXAPNaNRepeaterXNAXNAXNACredit and cash offices-1XNANaNXNACashNaNNaNNaNNaNNaNNaN
82367563342292Cash loansNaN0.00.0NaNNaNMONDAY15Y1NaNNaNNaNXNACanceled-386XNAXAPNaNRepeaterXNAXNAXNACredit and cash offices-1XNANaNXNACashNaNNaNNaNNaNNaNNaN
92579447334349Cash loansNaN0.00.0NaNNaNSATURDAY15Y1NaNNaNNaNXNACanceled-57XNAXAPNaNRepeaterXNAXNAXNACredit and cash offices-1XNANaNXNACashNaNNaNNaNNaNNaNNaN
SK_ID_PREVSK_ID_CURRNAME_CONTRACT_TYPEAMT_ANNUITYAMT_APPLICATIONAMT_CREDITAMT_DOWN_PAYMENTAMT_GOODS_PRICEWEEKDAY_APPR_PROCESS_STARTHOUR_APPR_PROCESS_STARTFLAG_LAST_APPL_PER_CONTRACTNFLAG_LAST_APPL_IN_DAYRATE_DOWN_PAYMENTRATE_INTEREST_PRIMARYRATE_INTEREST_PRIVILEGEDNAME_CASH_LOAN_PURPOSENAME_CONTRACT_STATUSDAYS_DECISIONNAME_PAYMENT_TYPECODE_REJECT_REASONNAME_TYPE_SUITENAME_CLIENT_TYPENAME_GOODS_CATEGORYNAME_PORTFOLIONAME_PRODUCT_TYPECHANNEL_TYPESELLERPLACE_AREANAME_SELLER_INDUSTRYCNT_PAYMENTNAME_YIELD_GROUPPRODUCT_COMBINATIONDAYS_FIRST_DRAWINGDAYS_FIRST_DUEDAYS_LAST_DUE_1ST_VERSIONDAYS_LAST_DUEDAYS_TERMINATIONNFLAG_INSURED_ON_APPROVAL
16702041407146198989Cash loans36598.095450000.0570073.5NaN450000.0THURSDAY12Y1NaNNaNNaNXNARefused-848Cash through the bankHCUnaccompaniedRepeaterXNACashx-sellCredit and cash offices100XNA24.0middleCash X-Sell: middleNaNNaNNaNNaNNaNNaN
16702052815130338803Cash loans14584.050135000.0182956.5NaN135000.0SATURDAY10Y1NaNNaNNaNXNARefused-1407Cash through the bankLIMITUnaccompaniedRepeaterXNACashwalk-inCredit and cash offices100XNA24.0highCash Street: highNaNNaNNaNNaNNaNNaN
16702062459206238591Cash loans19401.435180000.0243936.00.0180000.0TUESDAY13Y10.000000NaNNaNPurchase of electronic equipmentApproved-1833Cash through the bankXAPUnaccompaniedNewXNACashwalk-inCredit and cash offices100XNA24.0highCash Street: high365243.0-1802.0-1112.0-1112.0-1100.00.0
16702071662353443544Cash loans12607.875112500.0112500.00.0112500.0MONDAY10Y10.000000NaNNaNXNARefused-2514Cash through the bankSCOUnaccompaniedRepeaterXNACashwalk-inCredit and cash offices100XNA12.0highCash Street: highNaNNaNNaNNaNNaNNaN
16702081556789209732Cash loans22299.390315000.0436216.5NaN315000.0THURSDAY17Y1NaNNaNNaNXNAApproved-1279Cash through the bankXAPUnaccompaniedRefreshedXNACashx-sellCredit and cash offices100XNA36.0middleCash X-Sell: middle365243.0-1249.0-199.0-919.0-912.01.0
16702092300464352015Consumer loans14704.290267295.5311400.00.0267295.5WEDNESDAY12Y10.000000NaNNaNXAPApproved-544Cash through the bankXAPNaNRefreshedFurniturePOSXNAStone43Furniture30.0low_normalPOS industry with interest365243.0-508.0362.0-358.0-351.00.0
16702102357031334635Consumer loans6622.02087750.064291.529250.087750.0TUESDAY15Y10.340554NaNNaNXAPApproved-1694Cash through the bankXAPUnaccompaniedNewFurniturePOSXNAStone43Furniture12.0middlePOS industry with interest365243.0-1604.0-1274.0-1304.0-1297.00.0
16702112659632249544Consumer loans11520.855105237.0102523.510525.5105237.0MONDAY12Y10.101401NaNNaNXAPApproved-1488Cash through the bankXAPSpouse, partnerRepeaterConsumer ElectronicsPOSXNACountry-wide1370Consumer electronics10.0low_normalPOS household with interest365243.0-1457.0-1187.0-1187.0-1181.00.0
16702122785582400317Cash loans18821.520180000.0191880.0NaN180000.0WEDNESDAY9Y1NaNNaNNaNXNAApproved-1185Cash through the bankXAPFamilyRepeaterXNACashx-sellAP+ (Cash loan)-1XNA12.0low_normalCash X-Sell: low365243.0-1155.0-825.0-825.0-817.01.0
16702132418762261212Cash loans16431.300360000.0360000.0NaN360000.0SUNDAY10Y1NaNNaNNaNXNAApproved-1193Cash through the bankXAPFamilyRepeaterXNACashx-sellAP+ (Cash loan)-1XNA48.0middleCash X-Sell: middle365243.0-1163.0247.0-443.0-423.00.0